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contrastive learning has seen a boom of interest in self-supervised learning techniques especially in computer vision with papers like simclr moco and bootstrap your own latent these learning algorithms map representations of positive keys to be similar and negative keys to be dissimilar researchers from uc berkeley and docHub research designed a model that uses contrastive learning for the unpaired image to image translation problem fitting it into the generative adversarial network model this is where we have a set of source images like horses and a set of target images like zebras but they arent explicitly paired with one another in the training set there isnt a corresponding zebra for every horse image in the source data set the researchers use a patch level comparison where patches in the generated image that are produced by the generator should be similar to the patch at the same location as in the original image and dissimilar to all the other patches in that same original ima
