Tabular Data Generation using Binary Diffusion
Authors: name: Vitaliy Kinakh, name: Slava Voloshynovskiy

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.