A broad category of self-supervised learning techniques are those that use contrastive losses, which have been used in a wide range of computer vision applications like image similarity, dimensionality reduction (DrLIM) and face verification/identification. These methods learn a latent space that clusters positive samples together while pushing apart negative samples.