SMI: Efficient Self-Supervised Learning via Mutual-Information-Inspired Dependency Optimization
Self-supervised learning (SSL) has achieved remarkable representation learning performance, but many existing methods rely on large batch sizes, memory banks, momentum encoders, or global synchronization mechanisms that substantially increase computational cost and training complexity. In this work, we propose Semantic Mutual Information (SMI), a lightweight self-supervised objective derived from a mutual-information-inspired dependency formulation under Gaussian assumptions. Unlike conventional correlation matching objectives that operate on high-dimensional feature correlation matrices, SMI performs optimization on a sample-level dependency matrix through a nonlinear transformation of p...
| Authors | Pritam Mishra, Coloma Ballester, Dimosthenis Karatzas |
|---|---|
| Primary category | cs.CV |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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