RecTable：基于修正流的表格数据快速建模

RecTable: Fast Modeling Tabular Data with Rectified Flow

March 26, 2025

作者: Masane Fuchi, Tomohiro Takagi

cs.AI

摘要

基于分数或扩散模型生成高质量表格数据，其表现超越了基于GAN和VAE的模型。然而，这些方法需要大量的训练时间。本文中，我们提出了RecTable，它采用在文本到图像生成和文本到视频生成等领域应用的整流流建模技术。RecTable具有简洁的架构，仅由少量堆叠的门控线性单元块构成。此外，我们的训练策略同样简洁，融合了混合类型噪声分布和对数正态时间步分布。实验表明，与多种最先进的扩散和分数模型相比，RecTable在保持竞争力的同时显著减少了所需的训练时间。我们的代码已发布于https://github.com/fmp453/rectable。

English

Score-based or diffusion models generate high-quality tabular data, surpassing GAN-based and VAE-based models. However, these methods require substantial training time. In this paper, we introduce RecTable, which uses the rectified flow modeling, applied in such as text-to-image generation and text-to-video generation. RecTable features a simple architecture consisting of a few stacked gated linear unit blocks. Additionally, our training strategies are also simple, incorporating a mixed-type noise distribution and a logit-normal timestep distribution. Our experiments demonstrate that RecTable achieves competitive performance compared to the several state-of-the-art diffusion and score-based models while reducing the required training time. Our code is available at https://github.com/fmp453/rectable.