Meta Pseudo Labels

Hieu Pham, Zihang Dai, Qizhe Xie, Quoc V. Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 11557-11568

Abstract

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 kept fixed, in Meta Pseudo Labels, the teacher is constantly adapted by the feedback of how well the student performs on the labeled dataset. As a result, the teacher generates better pseudo labels to teach the student.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Pham_2021_CVPR, author = {Pham, Hieu and Dai, Zihang and Xie, Qizhe and Le, Quoc V.}, title = {Meta Pseudo Labels}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {11557-11568} }