  We present Meta Pseudo Labels, a semi-supervised learning method that
achieves a new state-of-the-art top-1 accuracy of 90.2% on ImageNet, which is
1.6% better than the existing state-of-the-art. Like Pseudo Labels, Meta Pseudo
Labels has a teacher network to generate pseudo labels on unlabeled data to
teach a student network. However, unlike Pseudo Labels where the teacher is
fixed, the teacher in Meta Pseudo Labels is constantly adapted by the feedback
of the student's performance on the labeled dataset. As a result, the teacher
generates better pseudo labels to teach the student. Our code will be available
at
https://github.com/google-research/google-research/tree/master/meta_pseudo_labels.
