Robust Diffusion Models via Divergence-Induced Weighted Denoising
We show that replacing the standard MSE denoising loss in diffusion models with a nonlinear transformation induced by an f-divergence yields a simple robust training surrogate that empirically improves performance under data contamination, with small additional computational overhead. The theoretical foundation rests on a local divergence construction: under the Gaussian reverse-kernel structure of DDPM, each per-step likelihood ratio follows a lognormal distribution parameterized by a scalar mismatch, so the conditional f-divergence at each step reduces to a one-dimensional function of the denoising error. Summing these local divergences yields a training objective that unifies diffusion...
| Authors | Lei Li, Yuexiao Dong |
|---|---|
| Primary category | stat.ML |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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