Deep Learning, Artificial Intelligence, Semi Supervised Learning, Image Classification, Data Augmentation.
data matches the distribution of the provided labeled data. A running average of the model’s predictions on unlabeled data is maintained, which is referred to as ~p(y). Given the model’s prediction q=pmodel(y|u,θ) on an unlabeled example u, q is scaled by the ratio p(y)/~p(y) and then renormalized