Self-supervised learning (SSL) has become a handy technique in computer vision tasks. Significant advances in SSL means its methods can now learn representations even if the input samples are distorted. Also referred to as ‘data augmentations’, this is made possible by maximising the similarity of representations extracted from different distorted versions of a sample. However,…

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