the representation can only include things that are not destroyed by the augmentations and if you construct the augmentations smartly that means you only retain the semantic information that's it so the loss function is pretty simple as you can see right here what you want is and this bar is a normalization what you want is the l2 norm between the this representation be close to the cue of that representation so the cue simply tries to predict the other representation and you do that for both ways so you once stick the image in