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frozen encoders

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papersTODAY 04:00 UTC

Sharp Rates and a One-Line Fix for Spectral Representation Learning

A new arXiv paper analyzes spectral methods for representation learning, where an encoder is trained once, frozen, and then reused by lightweight probes on downstream tasks. The authors derive tight convergence rates and propose a minimal, one-line correction that determines when off-the-shelf features suffice and when they need adjustment.

research/representation-learning/self-supervised/arxiv