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Hieu Pham Zihang Dai Qizhe Xie Minh-Thang Luong Quoc V. Le
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 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.
Code Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| image-classification-on-imagenet | Meta Pseudo Labels (EfficientNet-B6-Wide) | Number of params: 390M Top 1 Accuracy: 90% |
| image-classification-on-imagenet | Meta Pseudo Labels (ResNet-50) | Top 1 Accuracy: 83.2% |
| image-classification-on-imagenet | Meta Pseudo Labels (EfficientNet-L2) | Hardware Burden: 95040G Number of params: 480M Operations per network pass: Top 1 Accuracy: 90.2% Top 5 Accuracy: 98.8 |
| image-classification-on-imagenet-real | Meta Pseudo Labels (EfficientNet-L2) | Accuracy: 91.02% |
| image-classification-on-imagenet-real | Meta Pseudo Labels (EfficientNet-B6-Wide) | Accuracy: 91.12% |
| semi-supervised-image-classification-on-2 | Meta Pseudo Labels (ResNet-50) | Top 1 Accuracy: 73.89% Top 5 Accuracy: 91.38% |
| semi-supervised-image-classification-on-cifar | Meta Pseudo Labels (WRN-28-2) | Percentage error: 3.89± 0.07 |
| semi-supervised-image-classification-on-svhn | Meta Pseudo Labels (WRN-28-2) | Accuracy: 98.01 ± 0.07 |
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