title: Tabular Data Generation using Binary Diffusion
summary: 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.
paper_authors._id: 66f3f525832a9fc838b55bc5
paper_authors.name: Vitaliy Kinakh
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-09-25T11:53:22.720Z
paper_authors._id: 623732884f73a51ab0168c60
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1647784534798-noauth.jpeg
paper_authors.fullname: Vitaliy Kinakh
paper_authors.type: user
paper_authors.user: vitaliykinakh
paper_authors._id: 66f3f525832a9fc838b55bc6
paper_authors.name: Slava Voloshynovskiy
paper_title: Tabular Data Generation using Binary Diffusion
paper_summary: 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.
paper_ai_keywords: Binary Transformation Binary Diffusion XOR operations Binary Cross-Entropy Loss