same image are pulled close together and everything else is pushed apart right so that's why these are called positives and these are called negatives so the contrast of training basically means that you always want to have a set that you pull together in representation space and assets called the negatives that you push apart so the network basically learns about these random transformations that you have here the network kind of learns what it means to come from the same image it learns to be robust to these kind of transformations