and you see it you tell the classifier that it's predictions on these two different augmentations of the unlabeled data should be roughly similar because even though you don't have a label you tell the classifier that whatever it's printing it should be similar and this way the classifier gets a lot of structure and loss function and parameters that are being learned from unlabeled data a lot of constrains are being imposed and therefore it's going to be much more regularized than just training on the label data so this is a