situation where the network is really good already for a given sample so it already puts a dog into the dark class then the the gradient will not be pulling much for that sample it might mainly focuses on where you're still wrong so it is like I appreciate the analysis but it is not a notable difference I think what they want to show is that their loss if you do gradient descent really does what it is supposed to do namely first of all it does this polling together pushing a part of inner products for the positive and negative samples and it