data trying to really really good classifier and asset classifier to predict an unlabeled here and you infer the cereal labels on illegal data and you train a student model with the combined data of the teacher teacher of the original data label data that you use for the teacher as well as the guest labels on our label data and you add noise to this process through data augmentation dropout and stochastic depth which is another version of skip connections with stochastic and create a lot you know noisy student that's why