for each unlabeled image, is it be input to the different model with different data augmentation?

[KevinYu17]

I am confused about contrast learning. My current understanding is that for models A and B, the same unlabeled image is enhanced with different data augmentation A1 and B1, and the corresponding data is put into models A and B respectively for subsequent similarity comparison. Am I right? If so, how to compute the L-similarity between the different augmentation(what if it is rotated or flipped, the loss could be high)

If my comprehension is totally wrong, could you tell me the correct procedure?
Thanks a lot.