New Framework Evaluates Representation Learning in Diffusion Models Using Self-Supervised Principles
Researchers introduced a new framework for analyzing how diffusion models learn representations, decomposing features into invariant and residual components and creating a metric called Invariant Contamination Ratio (ICR). The study finds that invariance peaks at intermediate noise levels and that ICR can detect when models transition from learning to memorizing data. This work bridges understanding of how diffusion models simultaneously excel at both generation and representation learning.
A new arXiv paper presents a framework for jointly evaluating the representation and generation capabilities of diffusion models through self-supervised learning principles. The researchers decompose learned features into invariant and residual components, deriving the Invariant Contamination Ratio (ICR)—a Fisher-based metric that measures how residual variation contaminates invariant signal in feature space. Their analysis reveals that invariance peaks at intermediate noise levels, which also correspond to the best downstream classification performance. Additionally, the ICR metric serves as a sensitive indicator of when models transition from genuine generalization to memorization in data-limited scenarios, detectable from training features alone without requiring external evaluators or held-out test sets. The work demonstrates that diffusion models can be effectively monitored and understood through the geometric properties of their learned representations.
What different sources said
- arXiv cs.LGCenter
Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions
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