we learn to ignore that augmentation because our representation now needs to be predictable right cuny we learn q to predict the the representation under the expectation of our augmentations and that means it can't be dependent on one particular augmentation okay learns to ignore it so that's basically what's happening here again there is nothing keeping this from simply collapsing it to a trivial solution and it's probably a combination of the of the initialization and the learning procedure itself that it you