awesome-contrastive-self-supervised-learning
This collection provides a wide range of papers on contrastive self-supervised learning, useful for scholars and industry professionals. Regular updates ensure coverage of various topics such as topic modeling, vision-language representation, 3D medical image analysis, and multimodal sentiment analysis. Each paper entry includes links to the paper and code, if available, facilitating access to cutting-edge methods and experimental setups. Well-suited for those aiming to enhance their understanding of recent progress in contrastive learning, this collection serves as an essential reference for its comprehensive scope and pertinence.
