and then train a classifier on that new data set then use it to classify the real test set and if that classifier is able to classify the real test set they take that as evidence that their generator was pretty good if if it could be used to make a fake training set there are a few downsides to that procedure like for example if you were generating one mode way too often but you were still generating all the other modes occasionally your classifier might still be pretty good even though your generative model is screwed up but it