Noisy Student Training
Noisy Student Training is a semi-supervised learning method which achieves 88.4% top-1 accuracy on ImageNet (SOTA) and surprising gains on robustness and adversarial benchmarks. Noisy Student Training is based on the self-training framework and trained with 4 simple steps:
1.Train a classifier on labeled data (teacher).
2.Infer labels on a much larger unlabeled dataset.
3.Train a larger classifier on the combined set, adding noise (noisy student).
4.Go to step 2, with student as teacher
For ImageNet checkpoints trained by Noisy Student Training, please refer to the EfficientNet github.
github連結：https://github.com/google-research/noisystudent