labeled and unlabeled data the individual augmentations are used for generating a guest label we'll be seeing more about it label guessing for each unlabeled example mix match produces a guess for the examples label using the model's prediction this guess is later used in unsupervised lost term the entropy regularization to enforce the fact that the classified decision boundary should not pass through the high density region of marginal data distribution the classifier outputs low entropy prediction on unlabeled data this is done by adding