Self-supervised learning is cast as latent distribution matching that aligns representations to a model while enforcing uniformity, unifying multiple SSL families and proving identifiability for predictive variants even with nonlinear predictors.
Lifting archi- tectural constraints of injective flows.arXiv preprint arXiv:2306.01843
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Understanding Self-Supervised Learning via Latent Distribution Matching
Self-supervised learning is cast as latent distribution matching that aligns representations to a model while enforcing uniformity, unifying multiple SSL families and proving identifiability for predictive variants even with nonlinear predictors.