用二进制扩散模型生成表格数据，无需复杂预处理。
Tabular Data Generation using Binary Diffusion
- 将表格数据转为固定大小二进制表示，简化生成流程。
- 在Travel、Adult Income等数据集上超越现有模型，体积更小。
- 适合需要隐私保护和低资源部署的表格生成场景。
生成合成表格数据在机器学习中至关重要，尤其当真实数据稀缺或敏感时。传统生成模型因表格数据的混合类型和分布差异面临挑战，常需复杂预处理或大规模预训练模型。本文提出一种无损二进制转换方法，将任意表格数据转化为固定尺寸的二进制表示，并设计对应的新生成模型——二进制扩散（Binary Diffusion），专为二进制数据优化。该模型利用XOR操作进行噪声添加与移除，采用二元交叉熵损失训练。方法无需复杂预处理、噪声参数调优或大规模预训练。我们在多个主流表格基准数据集上评估，结果表明Binary Diffusion在Travel、Adult Income和Diabetes数据集上均优于现有最先进模型，且模型规模显著更小。代码与模型已公开于：https://github.com/vkinakh/binary-diffusion-tabular。
原文摘要 · Abstract (English)
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. Code and models are available at: https://github.com/vkinakh/binary-diffusion-tabular
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