Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. By clicking accept or continuing to use the site, you agree to the terms outlined in our. During the learning of the student, we inject noise such as dropout, stochastic depth, and data augmentation via RandAugment to the student so that the student generalizes better than the teacher. 27.8 to 16.1. Not only our method improves standard ImageNet accuracy, it also improves classification robustness on much harder test sets by large margins: ImageNet-A[25] top-1 accuracy from 16.6% to 74.2%, ImageNet-C[24] mean corruption error (mCE) from 45.7 to 31.2 and ImageNet-P[24] mean flip rate (mFR) from 27.8 to 16.1. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. We sample 1.3M images in confidence intervals. EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. The paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task is revisited, and a simple recipe that is called Big Transfer (BiT) is created, which achieves strong performance on over 20 datasets. Significantly, after using the masks generated by student-SN, the classification performance improved by 0.9 of AC, 0.7 of SE, and 0.9 of AUC. possible. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. all 12, Image Classification About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . If you get a better model, you can use the model to predict pseudo-labels on the filtered data. In terms of methodology, With Noisy Student, the model correctly predicts dragonfly for the image. Infer labels on a much larger unlabeled dataset. Zoph et al. But training robust supervised learning models is requires this step. We find that Noisy Student is better with an additional trick: data balancing. Different kinds of noise, however, may have different effects. Since a teacher models confidence on an image can be a good indicator of whether it is an out-of-domain image, we consider the high-confidence images as in-domain images and the low-confidence images as out-of-domain images. Semi-supervised medical image classification with relation-driven self-ensembling model. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. Noisy Students performance improves with more unlabeled data. First, a teacher model is trained in a supervised fashion. Self-training with Noisy Student. For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. We determine number of training steps and the learning rate schedule by the batch size for labeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 3.5B weakly labeled Instagram images. The abundance of data on the internet is vast. To achieve this result, we first train an EfficientNet model on labeled Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. See A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. We iterate this process by putting back the student as the teacher. . Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. Please refer to [24] for details about mCE and AlexNets error rate. et al. Works based on pseudo label[37, 31, 60, 1] are similar to self-training, but also suffers the same problem with consistency training, since it relies on a model being trained instead of a converged model with high accuracy to generate pseudo labels. on ImageNet ReaL. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. - : self-training_with_noisy_student_improves_imagenet_classification Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Iterative training is not used here for simplicity. We use EfficientNet-B4 as both the teacher and the student. It is expensive and must be done with great care. We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. to noise the student. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. Code is available at https://github.com/google-research/noisystudent. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. Their main goal is to find a small and fast model for deployment. Specifically, we train the student model for 350 epochs for models larger than EfficientNet-B4, including EfficientNet-L0, L1 and L2 and train the student model for 700 epochs for smaller models. We use the same architecture for the teacher and the student and do not perform iterative training. Use Git or checkout with SVN using the web URL. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. to use Codespaces. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet The performance drops when we further reduce it. To date (2020) we will introduce "Noisy Student Training", which is a state-of-the-art model.The idea is to extend self-training and Distillation, a paper that shows that by adding three noises and distilling multiple times, the student model will have better generalization performance than the teacher model. Please We use a resolution of 800x800 in this experiment. Our experiments show that an important element for this simple method to work well at scale is that the student model should be noised during its training while the teacher should not be noised during the generation of pseudo labels. Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. Yalniz et al. We also list EfficientNet-B7 as a reference. If nothing happens, download GitHub Desktop and try again. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. We do not tune these hyperparameters extensively since our method is highly robust to them. Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. Self-Training with Noisy Student Improves ImageNet Classification We present a simple self-training method that achieves 87.4 In particular, we first perform normal training with a smaller resolution for 350 epochs. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. (using extra training data). EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. The proposed use of distillation to only handle easy instances allows for a more aggressive trade-off in the student size, thereby reducing the amortized cost of inference and achieving better accuracy than standard distillation. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. The most interesting image is shown on the right of the first row. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. We apply dropout to the final classification layer with a dropout rate of 0.5.