that it's generalizing well to a new unlabeled data set or new validation set so typically people use weight decay dropout data argumentation for making sure that the classifiers generalize well and those are also pretty important in semi-supervised learning and methods that identity use these are unsupervised it augmentation or UDA and mix-match which we look at in detail but there are also other papers that we can't really cover in the scope of the structure but you should check out other people's that are related