[D] Representation Learning - Self-supervision methods that do well with a low number of classes

Web: https://www.reddit.com/r/MachineLearning/comments/sgv1xi/d_representation_learning_selfsupervision_methods/

I understand that a contrastive learning approach such as SimCLR has an inherent problem when dealing with a low number of classes (let's say 2,3,5,6, maybe even 10). Problem is that the chances of picking a negative sample that has the same label as the image from the positive pair is not low (let's say a dog and another dog)

Which contrastive learning approaches do better on such problems that we have let's say 4 classes rather than 1000 (or …

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