NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training
Training a diffusion model involves two sources of randomness for each data sample: the timestep and the Gaussian noise realization. The timestep has been studied extensively through scheduling and weighting, whereas the impact of the noise realization at a given timestep is still underexplored. In this work, we examine whether different noise instances are equally informative. We introduce NoiseRater, a network that scores an individual noise instance conditioned on the data sample and timestep. The rater is learned through bilevel optimization, where its scores reweight the diffusion loss in the inner loop, and it is updated to reduce validation loss after the inner-loop updates. Using ...
| Authors | Haokai Zhao, Da Xing, Hanqun Cao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 May 2026 |
| Last updated at source | 29 September 2026 |
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