SEMRes-DDPM: Residual Network Based Diffusion Modelling Applied to Imbalanced Data

no code implementations • 9 Mar 2024 • Ming Zheng, Yang Yang, Zhi-Hang Zhao, Shan-Chao Gan, Yang Chen, Si-Kai Ni, Yang Lu

In the current oversampling methods based on generative networks, the methods based on GANs can capture the true distribution of data, but there is the problem of pattern collapse and training instability in training; in the oversampling methods based on denoising diffusion probability models, the neural network of the inverse diffusion process using the U-Net is not applicable to tabular data, and although the MLP can be used to replace the U-Net, the problem exists due to the simplicity of the structure and the poor effect of removing noise.

Denoising