MATH · IN · MODELS

Fisher invariant-contamination ratio marks a diffusion model's optimal noise level

measured in 1 paper

- The Invariant Contamination Ratio (ICR) is a Fisher-based signal-to-noise measure that splits diffusion features into an invariant component (shared across noisy views) and a residual, scoring how much residual contaminates the invariant signal at each noise level. [li-etal-2026-evaluating-representation-space-diffusion-models] - ICR is minimized at intermediate noise levels, which coincide with the best linear-probe classification features (e.g. sigma ~0.29 for EDM, t ~0.2 for SiT). [li-etal-2026-evaluating-representation-space-diffusion-models] - ICR co-moves with FID in data-rich training and its rise anticipates the onset of training-data memorization, enabling label-free early stopping. [li-etal-2026-evaluating-representation-space-diffusion-models] - Tested on EDM, SiT-XL/2 and SiT-B/2 over CIFAR-10/100 and ImageNet-64/256 (Stable Diffusion is only cited as context, not used); observational-diagnostic, no causal intervention. [li-etal-2026-evaluating-representation-space-diffusion-models]

Context

invariant contamination ratio, noise-level feature invariance, memorization onset detection, self-supervised evaluation of generative models

Papers

Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles — Li, Xiao, Jia, Yixuan, Zhang, Zekai, Li, Xiang, Shi, Lianghe, Zhou, Jinxin, Zhu, Zhihui, Shen, Liyue, Qu, Qing2026 · arXiv:2606.09718