uniquely in this paper is that they're going to test model distillation when the student and the teacher have the same architecture or the same capacity the second idea is to fine tune the model with true labels only this is a pretty intuitive idea the weekly supervised label dataset has a ton of noise in it compared to the imagenet data set or you know other more specific label data sets a third idea is that large-scale unlabeled data is key to this performance naturally the key driver behind this algorithm is that