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Research questionHow should self-supervised visual learning combine objectives to prevent collapse while preserving semantic and spatial information?Without labels, visual objectives must learn invariance across augmented views while retaining useful spatial detail. Alignment signals can instead admit collapsed representations, making it difficult to preserve both kinds of information.
Computer Vision
Image & Video Processing
Machine Learning
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Three Necessary Principles for Self-Supervised Visual Representation LearningThe source studies self-supervised visual representation learning through semantic invariance across augmented views, patch-level spatial prediction, and representational non-degeneracy. It provides formal results on constant-encoder collapse, objective interactions, momentum encoders, and contrastive collapse resistance, alongside controlled experiments including patch retrieval; conclusions are reported at the scale studied.research paper · Sep 2, 2026
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