Deep Learning, Artificial Intelligence, Self Supervised Learning, Clustering, Contrastive Learning.
image features zt and zs from two different augmentations of the same image, their codes qt and qs are computed by matching these features to a set of K prototypes {c1, …, cK}. Then, a “swapped” prediction problem is setup with the following loss function (Eq. 1): where the function l(z, q)