diff --git "a/1tFAT4oBgHgl3EQfDBwd/content/tmp_files/load_file.txt" "b/1tFAT4oBgHgl3EQfDBwd/content/tmp_files/load_file.txt" new file mode 100644--- /dev/null +++ "b/1tFAT4oBgHgl3EQfDBwd/content/tmp_files/load_file.txt" @@ -0,0 +1,1503 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf,len=1502 +page_content='When Source-Free Domain Adaptation Meets Label Propagation Chunwei Wu1,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 ,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Guitao Cao1,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 ,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Yan Li3 and Xidong Xi1,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 and Wenming Cao4 and Hong Wang5 1Shanghai Key Laboratory of Trustworthy Computing,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' East China Normal University 2MOE Research Center for Software/Hardware Co-Design Engineering,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' East China Normal University 3Information,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Mechanical and Electrical Engineering,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Shanghai Normal University 4College of Information Engineering,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Shenzhen University 5Shanghai Research Institute of Microwave Equipment {52215902005,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 52265902004}@stu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='ecnu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='cn, gtcao@sei.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='ecnu.' metadata={'source': 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domain adaptation, where only a pre- trained source model is used to adapt to the target distribution, is a more general approach to achiev- ing domain adaptation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' However, it can be chal- lenging to capture the inherent structure of the tar- get features accurately due to the lack of super- vised information on the target domain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To tackle this problem, we propose a novel approach called Adaptive Local Transfer (ALT) that tries to achieve efficient feature clustering from the perspective of label propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' ALT divides the target data into inner and outlier samples based on the adaptive threshold of the learning state, and applies a cus- tomized learning strategy to best fits the data prop- erty.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Specifically, inner samples are utilized for learning intra-class structure thanks to their rela- tively well-clustered properties.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The low-density outlier samples are regularized by input consistency to achieve high accuracy with respect to the ground truth labels.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In this way, local clustering can be prevented from forming spurious clusters while ef- fectively propagating label information among sub- populations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Empirical evidence demonstrates that ALT outperforms the state of the arts on three public benchmarks: Office-31, Office-Home, and VisDA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 1 Introduction The excellent performance of deep learning relies heavily on a large amount of high-quality labeled data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Obtaining large amounts of manually labeled data for specific learning tasks is often time-consuming and expensive, making these tasks challenging to implement in practical applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To al- leviate this dependency, Unsupervised Domain Adaptation (UDA) has been developed to improve performance in the unlabeled target domain by exploiting the labeled source domain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Two popular practices for modern UDA design are learning domain-invariant features [Ganin et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2016;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Long et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2018;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Kang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2019;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Tang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2020] and generating dummy samples to match the target domain distri- bution [Wu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2020a;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Zhong et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Figure 1: A toy illustration of target feature distributions from the trained source model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The target samples can be divided into two subsets: the inner set and the outlier set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Different shapes represent different classes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' ALT achieves efficient clustering through Adap- tive Local-consistency Regularization (solid) and Adaptive Input- consistency Regularization (dashed).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Na et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' However, due to data privacy and secu- rity issues, the source domain training data required by most existing UDA methods is usually unavailable in real-world applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In response, Source-Free Domain Adaptation (SFDA) emerged, which attempted to adapt a trained source model to the target domain without using any source data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Due to the lack of source data, it is impossible to esti- mate source-target domain differences.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Existing theoretical work usually provides learning guarantees on the target do- main by further assuming that the source domain covers the support of the target domain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In the seminal work by [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021a], the authors point out that the target features from the source model have formed some semantic struc- tures.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Inspired by this intuition, we can preserve the im- portant clustering structure in the target domain by matching similar features in the high-dimensional space.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' However, the nearest-neighbor consistency of points in high-dimensional space may be wrong, such as when forcing the local consis- tency of points in low-density regions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' As shown in Table 1, when the source and target domains have significant differ- ences (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', Pr→Cl and Rw→Cl), numerous features gather in low-density regions, with only about one-third of the neigh- bors having the correct labels.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Along with such a question, we propose the Adaptive Local Transfer (ALT) shown in Fig- ure 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To achieve flexible adaptation for different data proper- ties and exploit the target domain structure information, our work introduces a novel data division strategy and then de- arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='08413v1 [cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='CV] 20 Jan 2023 Outlier Inner Inner Inner Outlier Inner LearningK Ar→Cl Ar→Pr Cl→Ar Pr→Cl Pr→Rw Rw→Cl 1 42.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 66.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 47.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='3 33.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='6 70.' metadata={'source': 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'/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='7 54.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 24.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='3 Table 1: Ratio (%) of different number of nearest neighbor which have the correct predicted label (on Office-Home).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' signs different regularization strategies to achieve label prop- agation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Firstly, our approach treats the target domain’s intrinsic structure information mining as a clustering problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Al- though existing local consistency-based methods aim to pre- serve the local structure, Table 1 illustrates the reason why neighbors are unreliable: In distance-based neighbor discrim- ination, neighbors are similar points in a high-dimensional space, and since the points in the low-density region are all scattered far apart, the label information in the K-nearest neighbors is not consistent at this point.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In ALT, we utilize the model’s learning state to dynamically divide the target data into inner and outlier sets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The intrinsic reason is that a sample can be considered an inner sample if it can obtain high predictive values from the classifier;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' otherwise, it is an outlier.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' We regularize the input consistency of outliers and encourage local consistency for those inner samples, which effectively improves the mining of intrinsic structural infor- mation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Secondly, we assume a minimum overlap in the subpop- ulations of the inner and outlier sets, and extend the subset using the simple but realistic extension assumption of [Wei et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For the inner set, the local-consistency regularizer connects similar points in the high-dimensional space, allow- ing SFDA training to proceed stably.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Enlightening experi- ments on Office-Home show that: (1) the pre-trained source model can extract rich semantic information from the target data;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' (2) what is lacking in domain adaptation is the filter- ing and permutation of high-dimensional semantic informa- tion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' We propose to recognize the clustering weights of each sample and reweight these samples, called Adaptive Local- consistency Regularization (ALR), to filter spurious cluster- ing information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To advance further along this line, we pro- pose Adaptive Input-Consistency Regularization (AIR) for the outlier set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Intuitively, different classes should adjust the thresholds based on the model’s learning state to encourage diverse predictions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Furthermore, as [Wei et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021] dis- cussed, a low-probability subset of data can be extended to a neighborhood with a large probability relative to that subset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' As a result, by customizing the learning strategy for differ- ent data properties, ALT can propagate structural information from the inner set to the outlier subset while also enhancing the clustering of the inner set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The contributions of this paper are summarized as follows: We introduce ALT, an adaptive clustering strategy for SFDA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Such a strategy customizes the learning strategy for data subsets by using dynamic data splits, allowing label information to propagate among subpopulations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To combat spurious clustering, we propose a novel Adaptive Local-consistency Regularization (ALR) strat- egy that estimates ground-truth structural information by re-weighting the neighbors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To utilize unlabeled data more effectively, we propose Adaptive Input-Consistent Regularization (AIR) from the perspective of label propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Such a regulariza- tion improves the clustering performance by propagating structural information from the inner set to the outlier set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Empirical evidence demonstrates that the proposed method outperforms the state-of-the-art on three domain adaptation benchmark datasets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 2 Related Work Source-free Domain Adaptation (SFDA).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' SFDA aims to adapt unlabeled target domains using only the pre-trained source model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Existing approaches try to refine the so- lution of SFDA by pseudo-labeling [Liang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Qiu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Huang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Ding et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Qu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Lee et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022], generating transition do- mains [Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2020b;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Kundu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Kundu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022], or local consistency [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021b;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021a;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' However, due to the domain differences, pseudo-labels that may contain noise may cause confirmation bias.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Additionally, task discrimina- tive information and domain-related information are highly nonlinearly entangled.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Directly constructing an ideal generic domain from the source model may be difficult.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Most closely related to our work is AaD[Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022], which intro- duced a simple and efficient optimization upper bound for feature clustering of unlabeled data, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', , aggregating (scat- tering) similar (dissimilar) features in the feature space.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' How- ever, AaD uses K-nearest neighbors directly, which may suf- fer from source bias due to domain shift.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In contrast to the above methods, we explore the idea of label propagation to assign regularization strategies to unlabeled data that are more suitable for the data properties, to achieve source-free model adaptation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Label Propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Label propagation has been widely used in semi-supervised learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' [Douze et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2018] show that label propagation on large image sets outperforms state- of-the-art few-shot learning when few labels are available.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' [Iscen et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2019] employ a transductive label propagation method based on the stream shape assumption to predict the entire dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' [Wei et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021] introduce the ”extension” assumption to analyze label propagation and show learning guarantees for unsupervised and semi-supervised learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' [Cai et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021] extend the extension assumption to domain adaptation and propose a provably effective framework for domain adaptation based on label propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Considering label propagation for SFDA and leveraging the advantages of extension assumptions, we design a novel and adaptive clus- tering strategy for SFDA that propagates structural informa- tion from high-density regions to low-density regions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Layer Ar→Cl Ar→Pr Ar→Rw Cl→Ar Cl→Pr Cl→Rw Pr→Ar Pr→Cl Pr→Rw Rw→Ar Rw→Cl Rw→Pr Avg.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Layer 4 (source) same class 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='355 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='464 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='378 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='305 0.' metadata={'source': 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'/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='332 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='536 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='440 across classes 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='060 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='014 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='009 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='031 0.' metadata={'source': 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'/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='002 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='023 Table 2: Cosine similarity within the same class and across classes on Office-Home.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Methods Ar→Cl Ar→Pr Ar→Rw Cl→Ar Cl→Pr Cl→Rw Pr→Ar Pr→Cl Pr→Rw Rw→Ar Rw→Cl Rw→Pr Avg.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' AaD (w/ Source Bottleneck Layer) 59.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='3 79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='3 82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='8 79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='5 67.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 57.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 58.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='5 85.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='7 AaD (w/ Target Bottleneck Layer) 69.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='3 85.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='7 91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 86.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 87.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='5 67.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='5 90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='5 89.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 92.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 Table 3: Comparison with different bottleneck layers on Office-Home.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 3 Method In this section, we first introduce the problem definition, our experiential motivation and theoretical analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Then, we propose ALT from the perspective of label propagation, claiming local consistency of inner samples with input con- sistency of outlier samples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 Preliminaries and Analysis Preliminary.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For source-free domain adaptation (SFDA), consider an unlabeled target dataset DT = {xi : xi ∈ X}Nt i=1 on the input space X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The task is to adapt a well-trained source model to the target domain without source data, where the target domain has the same C class as the source do- main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Following [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021a;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022], we use a feature extractor h : X → Z, and the classifier gc : Z → C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Then the output of the network is denoted as p(x) = δ(gc(h(x))) ∈ RC, where δ is the softmax func- tion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Specifically, we retrieve the nearest neighbors for each mini-batch of target features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Let F ∈ RNt×d denotes a memory bank that stores all target features and P ∈ RNt×C denotes the corresponding prediction scores in the memory bank, where d is the feature dimension in the last linear layer: F = [z1, z2, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' zNt] P = [p1, p2, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' pNt] (1) where zi is L2-normalized and pi denotes the output softmax probability for zi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Experiential motivation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Most of the clustering-based SFDA methods have the problem of spurious clustering.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Es- pecially, in extreme domain shifts, the spurious clustering problem worsens.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To address this issue, we investigate the local consistency of feature representations on the source and target domain models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' We carry out the experiments on Office-Home since it exists different degrees of domain shift, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', Rw vs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Pr and Pr vs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Cl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In this experiment, we use different network structures: (1) Layer 4: the last layer of the backbone network with 2048 feature dimensions;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' (2) Bottleneck: only replaces the bottleneck layer in the source model, with 256 feature dimensions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' It is worth noting that most of the existing clustering-based methods are distance- based.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The key idea is the smoothness assumption that the model should produce similar predictions for similar unla- beled data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Therefore, a good feature representation should have intra-class compactness and inter-class separability.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' It is very unexpected that the same-class similarity and across- class similarity between the source domain model and the tar- get domain model on Layer 4 are similar, while a huge differ- ence appears at the Bottleneck (see Table 2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' This means that adding a bottleneck layer in the model helps reduce redundant features, which improves discriminability and generalizabil- ity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Table 3 shows the learning effect of the AaD with only the bottleneck layer replaced.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Note that the bottleneck layer of the target model is only used for the analysis of this ex- periment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' We observe that replacing the target domain bot- tleneck layer improves the AaD model dramatically, from 72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='7% to 82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' This indicates that the high-dimensional features from Layer 4 of the source model already contain rich semantic information, whereas the generalization of the features is more reflected in the filtering and permutation of the semantic information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Additionally, on the results of AaD (w/ Source Bottleneck Layer), there was a very strong corre- lation between prediction accuracy and the ratio of same-class similarity to across-class similarity, as indicated by the Spear- man rank correlation of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='92.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' This observation hints that we can use the correlation between similarity and test accuracy to improve the clustering effect.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Theoretical analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Following the expansion assumption in [Wei et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Cai et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021], we first define that the suitable set of input transformations B(·) takes the gen- eral form B(x) ≜ {x′ : ∃A ∈ A such that ∥x′ − A(x)∥ ≤ r} for a small radius r > 0, where A can be understood as a distance-based neighborhood or the data augmentations set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Then, we define the neighborhood function N as N(x) = {x′ | B(x) ∩ B (x′) ̸= ∅} , (2) and the neighborhood of a set S ⊂ DT as N(S) ≜ ∪x∈SN(x).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' (3) The regularizer of gc is defined as: RB(gc) = EDT � max neighbor x′ 1 (gc(h(x)) ̸= gc(h (x′))) � (4) The expansion property on the target domain is defined as follows: Definition 1 (Constant Expansion [Wei et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021]).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' We say that distribution Q satisfies (q, ξ)-constant-expansion for some constant q, ξ ∈ (0, 1), if for all S ⊂ Q satisfying PQ(S) ≥ q, we have PQ[N(S)\\S] ≥ min {ξ, PQ[S]}.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Based on the model’s learning state, our ALT method di- vides the target data into the inner set (DI) and the outlier set (DO).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' By the Theorem 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='6 in [Wei et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021], suppose Q satisfies (1/2, ξ)-constant-expansion, then the classifier gc satisfies ϵT (gc) ≤ max � ξ ξ − 1, 2 � µ s.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='t.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' RB(gc) ≤ µ.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' (5) The expansion property implicitly states that if there is minimal overlap between the neighborhoods of DI and DO, labels can be propagated from DI to DO by the regularizer RB(gc).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 Overall Scheme Our ALT method divides the target data DT into inner set DI and outlier set DO by a dynamic threshold based on the model’s learning state.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' As mentioned before, the proposed ALT consists of two learning strategies: Adaptive Local- consistency Regularization (ALR) for the inner set and Adap- tive Input-consistency Regularization (AIR) for the outlier set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In Adaptive Local-consistency Regularization, inspired by the fact that the target features from the source model have formed some semantic structures, we treat the target do- main’s intrinsic structure information mining as a cluster- ing problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Since neighbors may provide wrong semantic information, we propose recognizing each sample’s cluster- ing weights.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' As observed in Table 2, the cosine similarity of same-class is generally higher than that of across-class.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Through this, we can measure neighbor affinity based on co- sine similarity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' By re-weighting with similarity-based adap- tive weights, we are able to promote positive clustering and combat spurious clustering.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Meanwhile, to improve sepa- rability between clusters, we employ the separation strategy proposed by [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022] to disperse the prediction of potentially dissimilar features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In Adaptive Input-consistency Regularization, we propa- gate the structural information from the inner set to the out- lier set via the extension assumption proposed by [Wei et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Since the outliers in the low-density region are far away from all other points, which means there is no nearest neighbor support, we turn to seek support from the outliers themselves.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Specifically, we perform label propagation by in- put consistency regularization Lair with adaptive thresholds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To encourage the model to produce diverse predictions, we employ the learning state of the model to generate adaptive thresholds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The overall optimization objective of ALT can be summa- rized as follows: L = Lalr + Lair + λLsep (6) where λ are a trade-off parameter.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='3 Adaptive Local Transfer Dataset Division.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In this work, we employ the model’s learning states to adaptively divide the data in DT into the inner sets DI and outlier sets DO.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' As believed in [Zhang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021], the learning effect of the model can be reflected by the class-level hit rate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Therefore, our principle is that the data division in ALT should be related to the prediction confidence of the unlabeled data on different classes so as to reflect the class-level learning status.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Namely, classes with fewer sam- ples reaching a threshold of prediction confidence are con- sidered to have difficult in learning local structural informa- tion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Moreover, the threshold should be increased steadily as the model is continuously improved during training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' We set the confidence threshold as the exponential moving average (EMA) of the highest confidence level for each training time step: τt = � 1 C , if t = 0 ατt−1 + (1 − α) max(p), otherwise (7) where α ∈ (0, 1) is the momentum decay of EMA, t de- notes the t-th iteration.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Combining this flexible thresholds, the learning effect of class c in the time step is defined as: σt(c) = Nt � n=1 1 (max (p) > τt) · 1 (arg max (p = c) .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' (8) Then we formulate the adaptive data division weights: Tt(c) = 1 C (1 − βt(c) log βt(c)) where, βt(c) = σt(c) maxc σt (9) Finally, the samples are dynamically grouped into the out- lier set in the t-th iteration: Dt O = {xi | max (pi) ≥ Tt(arg max (pi)), xi ∈ DT } , (10) and the inner samples are the rest target data, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', DI = DT \\DO.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To this end, we customize learning strategies for different data properties and connect both sets by extension assumption.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Adaptive Local-consistency Regularization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For the in- ner samples, since their features already have some seman- tic information, we can capture the intra-class structure by local-consistency regularization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' However, in the source-free domain adaptation problem, the features extracted by the pre- trained source model are typically influenced by the source bias.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To promote positive clustering and combat spurious clustering, we should find a technique to reveal the affinity of the samples and then re-weight them to approximate the ground-truth structural information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' As mentioned earlier, in clustering, not all of the neighbors have an equal affin- ity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Therefore, we use the distance information to estimate the weights and relax the ranking of samples in low-density regions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The Adaptive Local-consistency Regularization is as follows: Lalr = − NDI � i NCi � j wijpT i pj (11) where Ci denotes the K-nearest neighbor set of zi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The simi- larity weight wij in Eq.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 11 is the cosine similarity of zi to the neighbors zj, which is calculated via the memory bank F .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For clustering separability, we apply the separation strategy proposed in [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022] to push zi away from other features in mini-batches.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Lsep = − NDI � i NBi � m pT i pm (12) where Bi denotes other features except zi in mini-batch.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Adaptive Input-consistency Regularization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For outlier, since it is under-learned or hard-to-learn, we use the input consistency regularization to ensure that the model is locally consistent.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Specifically, we use a weakly augmented version of xi to generate the pseudo-label ˆpi = P(y | ω(xi)) and enforce consistency against its strongly augmented version Ω(xi).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To encourage the model to make diverse predictions, we combined regularization with the aforementioned class- level confidence thresholds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The Adaptive Input-consistency Regularization is as follows: Lair = 1 NDO NDO � i=1 H(ˆpi, qi) (13) where qi = P(y | Ω(xi)) is denote the pseudo label of Ω(xi).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 4 Experiments In this section, we evaluate the proposed method for SFDA on three popular domain adaptation benchmarks, compared with recent state-of-the-art SFDA methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 Datasets Office-31 [Saenko et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2010] is a commonly used dataset for domain adaptation that consists of three domains: Ama- zon (A), Webcam (W), and DSLR (D), each containing 31 categories of items in an office environment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Office-Home [Venkateswara et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2017] is a standard do- main adaptation dataset collected in office and home environ- ments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' It consists of four domains, Art (Ar), Clipart (Cl), Product (Pr), and RealWorld (Rw), and each covering 65 ob- ject categories.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' VisDA [Peng et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2017] is one of the large benchmark datasets on the domain adaptation task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' It contains 12 cate- gories of images from two subsets: synthetic image domain and real image domain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Methods Source-free A→D A→W D→W W→D D→A W→A Avg.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' ResNet-50 [He et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2016] \x17 68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} 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metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021] \x13 94.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='7 92.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='5 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 100.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='7 89.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='8 CPGA [Qiu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021] \x13 94.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 94.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='8 76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='6 89.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 SFDA-DE [Ding et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022] \x13 96.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 94.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='5 99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='8 76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='6 75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='5 90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 AaD [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022] \x13 96.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 92.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 100.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='5 89.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 feat-mixup [Kundu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022] \x13 94.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='6 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 100.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 78.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='3 78.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='7 ours \x13 96.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 100.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 78.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='5 Table 4: Accuracy (%) on Office-31 (ResNet-50).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 Setup Implementation details.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Following the standard protocol for SFDA, we use all labeled source data to obtain pre-trained models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For the Office-31 and Office-Home, the backbone network is ResNet-50 [He et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2016].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For VisDA, the back- bone network is ResNet-101.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For a fair comparison, we use the same network structure as SHOT [Liang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2020], NRC [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021a] and AaD [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' All network parameters are updated by Stochastic Gradient De- scent (SGD) with momentum of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9, an initial learning rate of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='001, and a weight decay of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='005.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The learning rate of the additional layer is 10 times smaller than that of the backbone layer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' We follow G-SFDA [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021b], NRC [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021a], and AaD [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022] for the number of nearest neighbors (K): set 3 for Office-31, Office-Home, and 5 on VisDA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To ensure a fair comparison, we set the hyper- parameter λ to be the same as in the previous work [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' That is, we set λ = � 1 + 10 ∗ iter maxiter �−β , and set β to 0 on Office-Home, 2 on Office-31, and 5 on VisDA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The strong augmentation function used in our experiments is RandAugment [Cubuk et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2020].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Baselines.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To empirically validate the effectiveness of our approach, we compared the ALT to the following base- line: (1) source-present DA methods: CDAN [Long et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2018], MDD [Zhang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2019], CAN [Kang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2019], SAFN [Xu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2019], MCC [Jin et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2020], SRDC [Tang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2020], FixBi [Na et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021];' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' (2) source-free DA methods: SHOT [Liang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2020], 3C-GAN [Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2020b], A2-Net [Xia et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021], NRC [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021a], HCL [Huang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021], CPGA [Qiu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021], SFDA- DE [Ding et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022], AaD [Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022] and feat- mixup [Kundu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='3 Results and Analysis In this section, we will present our results and compare with other methods, which are summarized in Table 4, 5, 6, re- spectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For a fair comparison, all baseline results were obtained from their original papers or the follow-up work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Comparison with state-of-the-art methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For Office- 31, as shown in Table 4, the proposed ALT yield state-of- the-art performance on 4 out of 6 tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Note that our ALT Methods Source-free Ar→Cl Ar→Pr Ar→Rw Cl→Ar Cl→Pr Cl→Rw Pr→Ar Pr→Cl Pr→Rw Rw→Ar Rw→Cl Rw→Pr Avg.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' ResNet-50 [He et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2016] \x17 34.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 50.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 58.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 37.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 41.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 46.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 38.' metadata={'source': 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'/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='3 ours \x13 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 86.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 78.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 97.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='6 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='8 91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='8 84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='8 96.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='6 94.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='7 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='7 53.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='3 88.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='7 Table 6: Accuracy (%) on VisDA (ResNet-101).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' produces competitive results even when compared to source- present methods such as FixBi (91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='5% v.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='s.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4%).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For Office-Home, Table 5 presents that the proposed ALT method achieves the most advanced classification accuracy (76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1%) and achieves the highest results on 7 out of 12 tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' As we all know, in clustering-based methods, the clustering error in- creases with the number of object classes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Therefore, it is difficult for local consistency-based SFDA methods to accu- rately capture the target structure information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' However, our ALT employs input consistency regularization to efficiently utilize unlabeled data through label propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' This is the primary reason for our success on Office-Home.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Moreover, ALT beats several source-present DA methods, such as SRDC and FixBi, by a large margin, which means that even if we do not have access to the source data, our method can still exploit the target structure information to achieve better adaptation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Similar observations on VisDA can be found in Table 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The reported results sufficiently demonstrate the superiority of our method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Comparison with clustering-based Method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' As dis- cussed in related work, NRC uses reciprocal nearest neigh- bors to measure clustering affinity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The improvement of our method indicates that our adaptive local consistency regular- ization makes more effective use of intra-class structural in- AaD Lalr Lair A→D A→W D→A W→A Avg.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' \x13 96.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 92.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='5 85.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 \x13 \x13 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='3 77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='6 86.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 \x13 \x13 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='8 94.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='7 79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='8 86.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9 \x13 \x13 96.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1 80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 78.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='2 87.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4 Table 7: Ablation study on Office-31.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' formation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Compared with AaD, our ALT improves the accu- racy by 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='6% on Office-31 and by 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1% on Office-Home, in- dicating that the co-training of the local consistency regular- izer and the input consistency regularizer performs reliable la- bel propagation through the subpopulation of unlabeled data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Visualization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To demonstrate the superiority of our method, we show the t-SNE feature visualization and con- fusion matrix on Office-31 (see Figure 2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' From Figures 2(a- d), we can observe that the clustering of the target features is more compact after the adaptation by ALT.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Figures 2(b) and (d) illustrate that ALT can achieve good model adaptation whether the model is pre-trained on a large-scale or small- scale source domain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In particular, when significant domain differences exist (as shown in Figure 2(c)), abundant target features are jumbled together, so that the model has difficult in capturing the local structure.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The flexible data division of (a) source-only (A→W) (b) ALT (A→W) (c) source-only (D→A) (d) ALT (D→A) (e) source-only (D→A) (f) ALT (D→A) Figure 2: The t-SNE and Confusion Matrix visualization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Figures (a-d): t-SNE visualization of the final prediction layer activation for source model and ALT, where red and blue points denote the source and target domains, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Note that the source samples are only used to plot the t-SNE.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Figures (e) and (f): The Confusion Matrix visualization for source model and ALT.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Best viewed in color.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' our method, thus, customizes the learning strategy for differ- ent data properties, which benefits the estimation of ground- truth structural information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The comparison of Figure 2(e) and (f) further demonstrates that our method increases predic- tion diversity by adaptively adjusting the training on under- learned or hard-to-learn samples (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', outlier).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Ablation Study.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To evaluate the contribution of the differ- ent components of our work, we conduct ablation studies for ALT on Office-31.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' We investigated different combinations of the two parts: Adaptive Local-consistency Regularization (ALR) and Adaptive Input-consistency Regularization (AIR).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Compared to our method, AaD can be regarded as the base- line.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' As shown in Table 7, each part of our method con- tributes to improving performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' It is not difficult to find that AIR contributes the most to the improvement of accu- racy, with the performance increasing from 85.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0% to 86.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='9%, which shows the effectiveness of label propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' ALR also improves the average performance by 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='1% compared to the base model, confirming that the distance-based reweighting improves the quality of the neighbors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For easy transfer tasks, target features from pre-trained source models naturally have good clustering performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In this case, ALR dominates in loss optimization, with AIR helping to improve model train- ing for under-learned categories.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' When the target feature dis- tribution is scattered, it benefits from the AIR to ensure the smoothness of the model, while the extended property am- plifies it to global consistency within the same class, allow- ing the limited structural information captured from the ALR to be propagated among subpopulations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Overall, ALT in- creased baseline AaD by an average of 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='4%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' This shows that there is complementarity between ALR and AIR.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' 5 Conclusions In this paper, we propose a novel approach called Adaptive Local Transfer (ALT), which tries to achieve efficient feature clustering from the perspective of label propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' ALT di- vides the target data into inner and outlier samples based on the adaptive threshold of the learning state, and applies a cus- tomized learning strategy to fit the data properties best.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' To mitigate the source bias, on the one hand, considering the clustering affinity, we propose Adaptive Local-consistency Regularization (ALR) to reduce spurious clustering by re- weighting neighbors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' On the other hand, Adaptive Input- consistency Regularization (AIR) is used at outlier points to propagate structural information from high-density to low- density regions, thus achieving high accuracy with respect to the ground truth labels.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Moreover, this co-training process can encourage positive clustering and combat spurious clus- tering.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' The experimental results of three popular benchmarks verify that our proposed model outperforms the state-of-the- art in various SFDA tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' For future work, we plan to ex- tend our ALT method to source-free open-set and partial-set domain adaptation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Acknowledgements This work was supported by the National Natural Science Foundation of China under Grant 61871186 and 61771322.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' References [Cai et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2021] Tianle Cai, Ruiqi Gao, Jason D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Lee, and Qi Lei.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' A theory of label propagation for subpopulation shift.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In ICML, volume 139 of Proceedings of Machine Learning Research, pages 1170–1182.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' PMLR, 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' [Cubuk et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2020] Ekin Dogus Cubuk, Barret Zoph, Jonathon Shlens, and Quoc Le.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Randaugment: Practical ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='CCC100 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 ' metadata={'source': 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'/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='0 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='20 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='10 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='30 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content='Predictionautomated data augmentation with a reduced search space.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In NeurIPS, 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' [Ding et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=', 2022] Ning Ding, Yixing Xu, Yehui Tang, Chao Xu, Yunhe Wang, and Dacheng Tao.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' Source-free domain adaptation via distribution estimation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' In CVPR, pages 7202–7212.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} +page_content=' IEEE, 2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tFAT4oBgHgl3EQfDBwd/content/2301.08413v1.pdf'} 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