jason718/awesome-self-supervised-learning

Comprehensively catalogs self-supervised learning papers across domains—computer vision, NLP, robotics, graph networks, and time-series—with links to PDFs and implementations. Organizes methods by approach (contrastive learning, generative models, pretext tasks) and includes theoretical foundations, benchmark codebases, and video/3D representation techniques. Covers both foundational architectures and recent developments, serving as a resource aggregator for researchers implementing SSL across PyTorch/TensorFlow ecosystems.

6,370 stars.