linearly extrapolates to decide that if you move a little bit in one direction then moving very far in that direction will give you more and more realistic samples it's important to use batch normalization most layers of the model and I won't go into batch normalization in detail but the idea is you take a full batch of input samples and you normalize the features of the network by subtracting the mean of those features across the whole batch and dividing by their standard deviation this makes the learning process a lot better