Tabular Data Generation using Binary Diffusion
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.
66f3f525832a9fc838b55bc5
Vitaliy Kinakh
extracted_confirmed
2024-09-25T11:53:22.720Z
623732884f73a51ab0168c60
https://cdn-avatars.huggingface.co/v1/production/uploads/1647784534798-noauth.jpeg
Vitaliy Kinakh
user
vitaliykinakh
66f3f525832a9fc838b55bc6
Slava Voloshynovskiy
Tabular Data Generation using Binary Diffusion
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.
A novel Binary Diffusion generative model for tabular data uses binary representations and XOR operations, outperforming existing models with less preprocessing and smaller size.
binary transformation Binary Diffusion XOR operations binary cross-entropy loss