of a student classification network to maximize its resulting validation set accuracy this is done by training the classification model or student network on pseudo labels labeled by a teacher network the teacher network is then updated to maximize the classification models accuracy on the validation set after it trains and updates itself through back propagation supervised learning on the pseudo labels from the teacher network this involves an interesting gradient through a gradient operation to train the teacher network