Papers › A High-Quality Robust Diffusion Framework for Corrupted Dataset
A High-Quality Robust Diffusion Framework for Corrupted Dataset
Quan Dao, Binh Ta, Tung Pham, Anh Tran
Developing image-generative models, which are robust to outliers in the training process, has recently drawn attention from the research community. Due to the ease of integrating unbalanced optimal transport (UOT) into adversarial framework, existing works focus mainly on developing robust frameworks for generative adversarial model (GAN). Meanwhile, diffusion models have recently dominated GAN in various tasks and datasets. However, according to our knowledge, none of them are robust to corrupted datasets. Motivated by DDGAN, our work introduces the first robust-to-outlier diffusion. We suggest replacing the UOT-based generative model for GAN in DDGAN to learn the backward diffusion process. Additionally, we demonstrate that the Lipschitz property of divergence in our framework contributes to more stable training convergence. Remarkably, our method not only exhibits robustness to corrupted datasets but also achieves superior performance on clean datasets.
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Code
Syntology Ran 5 of 12 code samples harvested from 1 repository linked to this paper; 7 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 1 ran · our draft was wrong; 2 ran with no contract checked.
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Code Syntology ran Syntology
12 samples harvested; 5 ran; 1 honoured the contract we drafted; 7 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Generation | CIFAR-10 | RDUOT | FID | 2.95 | #24 of 78 | Archive leaderboard | report |
| Image Generation | CelebA-HQ 256x256 | RDUOT | FID | 5.6 | #5 of 19 | Archive leaderboard | report |
| Image Generation | CelebA-HQ 256x256 | RDUOT | Recall | 0.38 | #5 of 19 | Archive leaderboard | report |
| Image Generation | STL-10 | RDUOT | FID | 11.5 | #3 of 31 | Archive leaderboard | report |
| Image Generation | STL-10 | RDUOT | Recall | 0.49 | #3 of 31 | Archive leaderboard | report |
| Image Generation | STL-10 | RDGAN | FID | 13.07 | #10 of 31 | Archive leaderboard | report |
| Image Generation | STL-10 | RDGAN | Recall | 0.47 | #10 of 31 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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