[D] How do contrastive loss functions avoid the issue of duplicates / near duplicates?

Contrastive loss functions work as follows: You have a grid of (image, image) (SiMCLR), or (image, text) pairs (CLIP), and you maximize the similarity between the target pairs (2 different crops of an image in the (image, image) case), or (image, text) pairs in the (image, text) case.

My question is, in the (image, image) case, how do you deal with the following issue: imagine you have 2 images that are near duplicates of each other. In this case, the …

loss machinelearning near