therefore having lower entropy in the output probability distribution of the classes and therefore minimizing the entropy of your classifier on unlabeled data I can do taking a classifiers outputs an unlabeled layer if they are confident enough you're trying to train on its own predictions so it has a similar effect and mathematically it's shown in these older papers that have been linked here so you can go and check it out and the next thing we're gonna see is later augmentation for label consistency so you take an image let's