A popular and useful framework for contrastive self-supervised learning known as SimCLR was introduced by Chen et. al.. The framework simplifies previous contrastive methods to self-supervised learning, and at the time was state-of-the-art at unsupervised image representation learning. The main simplification lies in the fact that SimCLR requires no specialised modules or additions to the architecture such as memory banks.
self-supervised learning computer vision deep learning