self training with noisy student improves imagenet classification

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. Self-Training With Noisy Student Improves ImageNet Classification. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Self-training Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. To intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct. When data augmentation noise is used, the student must ensure that a translated image, for example, should have the same category with a non-translated image. Our procedure went as follows. Agreement NNX16AC86A, Is ADS down? Imaging, 39 (11) (2020), pp. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Abdominal organ segmentation is very important for clinical applications. [57] used self-training for domain adaptation. The learning rate starts at 0.128 for labeled batch size 2048 and decays by 0.97 every 2.4 epochs if trained for 350 epochs or every 4.8 epochs if trained for 700 epochs. A. Alemi, Thirty-First AAAI Conference on Artificial Intelligence, C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, EfficientNet: rethinking model scaling for convolutional neural networks, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, A. Vedaldi, M. Douze, and H. Jgou, Fixing the train-test resolution discrepancy, V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), J. Weston, F. Ratle, H. Mobahi, and R. Collobert, Deep learning via semi-supervised embedding, Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le, Unsupervised data augmentation for consistency training, S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, I. E. Arazo, D. Ortego, P. Albert, N. E. OConnor, and K. McGuinness, Pseudo-labeling and confirmation bias in deep semi-supervised learning, B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson, There are many consistent explanations of unlabeled data: why you should average, International Conference on Learning Representations, Advances in Neural Information Processing Systems, D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, MixMatch: a holistic approach to semi-supervised learning, Combining labeled and unlabeled data with co-training, C. Bucilu, R. Caruana, and A. Niculescu-Mizil, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Y. Carmon, A. Raghunathan, L. Schmidt, P. Liang, and J. C. Duchi, Unlabeled data improves adversarial robustness, Semi-supervised learning (chapelle, o. et al., eds. However, in the case with 130M unlabeled images, with noise function removed, the performance is still improved to 84.3% from 84.0% when compared to the supervised baseline. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. A. Krizhevsky, I. Sutskever, and G. E. Hinton, Temporal ensembling for semi-supervised learning, Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks, Workshop on Challenges in Representation Learning, ICML, Certainty-driven consistency loss for semi-supervised learning, C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, R. G. Lopes, D. Yin, B. Poole, J. Gilmer, and E. D. Cubuk, Improving robustness without sacrificing accuracy with patch gaussian augmentation, Y. Luo, J. Zhu, M. Li, Y. Ren, and B. Zhang, Smooth neighbors on teacher graphs for semi-supervised learning, L. Maale, C. K. Snderby, S. K. Snderby, and O. Winther, A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, Towards deep learning models resistant to adversarial attacks, D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten, Exploring the limits of weakly supervised pretraining, T. Miyato, S. Maeda, S. Ishii, and M. Koyama, Virtual adversarial training: a regularization method for supervised and semi-supervised learning, IEEE transactions on pattern analysis and machine intelligence, A. Najafi, S. Maeda, M. Koyama, and T. Miyato, Robustness to adversarial perturbations in learning from incomplete data, J. Ngiam, D. Peng, V. Vasudevan, S. Kornblith, Q. V. Le, and R. Pang, Robustness properties of facebooks resnext wsl models, Adversarial dropout for supervised and semi-supervised learning, Lessons from building acoustic models with a million hours of speech, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), S. Qiao, W. Shen, Z. Zhang, B. Wang, and A. Yuille, Deep co-training for semi-supervised image recognition, I. Radosavovic, P. Dollr, R. Girshick, G. Gkioxari, and K. He, Data distillation: towards omni-supervised learning, A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, Semi-supervised learning with ladder networks, E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, Proceedings of the AAAI Conference on Artificial Intelligence, B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. Classification of Socio-Political Event Data, SLADE: A Self-Training Framework For Distance Metric Learning, Self-Training with Differentiable Teacher, https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. This result is also a new state-of-the-art and 1% better than the previous best method that used an order of magnitude more weakly labeled data[44, 71]. Self-training with Noisy Student improves ImageNet classificationCVPR2020, Codehttps://github.com/google-research/noisystudent, Self-training, 1, 2Self-training, Self-trainingGoogleNoisy Student, Noisy Studentstudent modeldropout, stochastic depth andaugmentationteacher modelNoisy Noisy Student, Noisy Student, 1, JFT3ImageNetEfficientNet-B00.3130K130K, EfficientNetbaseline modelsEfficientNetresnet, EfficientNet-B7EfficientNet-L0L1L2, batchsize = 2048 51210242048EfficientNet-B4EfficientNet-L0l1L2350epoch700epoch, 2EfficientNet-B7EfficientNet-L0, 3EfficientNet-L0EfficientNet-L1L0, 4EfficientNet-L1EfficientNet-L2, student modelNoisy, noisystudent modelteacher modelNoisy, Noisy, Self-trainingaugmentationdropoutstochastic depth, Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores., 12/self-training-with-noisy-student-f33640edbab2, EfficientNet-L0EfficientNet-B7B7, EfficientNet-L1EfficientNet-L0, EfficientNetsEfficientNet-L1EfficientNet-L2EfficientNet-L2EfficientNet-B75. labels, the teacher is not noised so that the pseudo labels are as good as It is expensive and must be done with great care. 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. This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. There was a problem preparing your codespace, please try again. Similar to[71], we fix the shallow layers during finetuning. We iterate this process by Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. Flip probability is the probability that the model changes top-1 prediction for different perturbations. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. In particular, we first perform normal training with a smaller resolution for 350 epochs. The algorithm is basically self-training, a method in semi-supervised learning (. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. We also study the effects of using different amounts of unlabeled data. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. If nothing happens, download Xcode and try again. The results also confirm that vision models can benefit from Noisy Student even without iterative training. The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . We used the version from [47], which filtered the validation set of ImageNet. 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. Self-training with Noisy Student improves ImageNet classification. We also list EfficientNet-B7 as a reference. [^reference-9] [^reference-10] A critical insight was to . During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Work fast with our official CLI. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 27.8 to 16.1. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. This material is presented to ensure timely dissemination of scholarly and technical work. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. IEEE Trans. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks.

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