awesome-self-supervised-learning

Why we included this project

Self-supervised learning now spans so many subfields that keeping up with the papers is a job in itself, and this index is built to make that easier. It sorts research into theory, computer vision (image, video, 3D, geometry, audio), reinforcement learning, robotics, NLP, speech recognition, time-series, and graph learning, with each entry linking to the PDF and, where available, the official code. The organization is what makes it worth keeping open: you can trace how contrastive methods evolved or find a benchmark implementation to build on without re-searching the literature. It is a community-maintained reading list rather than a software package, so treat it as a map of the field and a starting point for locating the actual repositories and papers you would run.