SwAV (Swapped Augmentations and Views) is a new method for teaching computers to understand images without needing humans to label everything in them. Instead, it learns from a large collection of unlabeled pictures. SwAV does this by teaching the computer to notice similarities and differences between different versions of the same picture. By doing this, it creates a map of how images are related to each other in a computer's mind. This map can then be used to help computers recognize objects in new pictures, even if they haven't seen those exact objects before. SwAV is exciting because it helps computers learn a lot from images all on their own, without needing a lot of help from humans.
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