you'll do is you'll use noise and one of the types of noise is that you severely augment this data right here in order to train the student now we've known for a long time that data augmentation for example in the frameworks of self-supervised learning and so on can have a very large benefit to training and here the fact that we incorporate this at extra data and we use noise and augmentations on it is going to result in a student that can sort of learn more about the data than than the teacher did know okay this this is basically it and