RecTable: Fast Modeling Tabular Data with Rectified Flow
Authors: name: Masane Fuchi, name: Tomohiro Takagi

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.