我们提出了Meta伪标签，这是一种半监督学习方法，可在ImageNet上实现90.2％的最新top-1准确性，比现有的最新水平提高1.6％。像伪标签一样，元伪标签也有一个教师网络，可以在未标记的数据上生成伪标签，以教授学生网络。..

Meta Pseudo Labels

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.