这篇论文改进了扩散模型的抗污染能力，用f-散度加权去噪在CIFAR-10上30%污染时FID从93降到77，比常用鲁棒损失好用。
研究者提出用f-散度诱导的非线性变换替换扩散模型的标准MSE去噪损失，在CIFAR-10数据集30%污染条件下，负指数散度(NED)将FID从93.0（KL散度）降至77.5。该方法基于局部散度构造，利用DDPM高斯反向核结构将每步条件f-散度简化为去噪误差的一维函数。Hellinger散度产生显式指数权重，连接至稳健M估计框架。实验显示NED优于Huber损失和截断MSE等常见鲁棒损失。
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 training as divergence induced weighted denoising, where the derivative of the induced divergence acts as a residual-space influence weight that controls the contribution of each sample. Bounded-influence divergences (Hellinger, negative exponential) suppress large error samples, with Hellinger yielding an explicit exponential weight, connecting the framework to robust M-estimation. Empirically, on CIFAR-10 under 30% contamination, NED reduces FID from 93.0 (KL) to 77.5, while also outperforming standard robust losses such as Huber and clipped MSE.