Three Necessary Principles for Self-Supervised Visual Representation Learning

We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives: semantic invariance across augmented views, patch-level spatial prediction, and representational non-degeneracy. We formalize these as the observation, prediction, and regularization principles and prove (i) that combining observation and prediction without regularization admits the constant encoder as a global minimizer under negative-free alignment;

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ethics.ai (8 August 2026), “Three Necessary Principles for Self-Supervised Visual Representation Learning,” evidence record 18330, https://ethics.ai/record/18330 (originally published by arXiv cs.LG).

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