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arxiv:2409.13882

Tabular Data Generation using Binary Diffusion

Published on Sep 20
· Submitted by vitaliykinakh on Sep 25
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Abstract

Generating synthetic tabular data is critical in machine learning, especially when real data is limited or sensitive. Traditional generative models often face challenges due to the unique characteristics of tabular data, such as mixed data types and varied distributions, and require complex preprocessing or large pretrained models. In this paper, we introduce a novel, lossless binary transformation method that converts any tabular data into fixed-size binary representations, and a corresponding new generative model called Binary Diffusion, specifically designed for binary data. Binary Diffusion leverages the simplicity of XOR operations for noise addition and removal and employs binary cross-entropy loss for training. Our approach eliminates the need for extensive preprocessing, complex noise parameter tuning, and pretraining on large datasets. We evaluate our model on several popular tabular benchmark datasets, demonstrating that Binary Diffusion outperforms existing state-of-the-art models on Travel, Adult Income, and Diabetes datasets while being significantly smaller in size.

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The paper introduces a novel method for generating synthetic tabular data using a novel Binary Diffusion model. It transforms tabular data into fixed-size binary representations and employs XOR operations and binary cross-entropy loss for training. This approach simplifies preprocessing, avoids large pretrained models, and achieves state-of-the-art results on benchmark datasets like Travel, Adult Income, and Diabetes while maintaining a smaller model size.

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