Why Self-Supervised Encoders Want to Be Normal
Self-supervised learning has achieved remarkable empirical success in learning robust representations without explicit labels, most recently demonstrated within the framework of Joint-Embedding Predictive Architectures (JEPA). However, a fundamental question remains: what analytical principles drive these encoders toward specific distributional states? In this paper, we demonstrate that the preference for normal distributions in self-supervised encoders is a direct consequence of the Information Bottleneck (IB) principle. By recasting the IB objective as a rate-distortion problem over the predictive manifold, we provide a theoretical basis for why optimal, target-neutral, latent represent...
| Authors | Yuval Domb |
|---|---|
| Primary category | cs.IT |
| Published | 30 April 2026 |
| Last updated at source | 3 May 2026 |
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