into two versions of that same image and when I then embed them linear neural network the symbol networking the left hand the right upper channels then the embedding should be close as measured with some cosine similarity and of course over another image that I embed then I'm betting should be far away and those are the negatives in the denominator so for more details and that of course go back to our self supervised learning lectures from a few weeks ago what's important here is done this is a very simple idea it's just saying turn an