. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. 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. Self-training first uses labeled data to train a good teacher model, then use the teacher model to label unlabeled data and finally use the labeled data and unlabeled data to jointly train a student model. 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 Noisy Student Training is based on the self-training framework and trained with 4-simple steps: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin combination of labeled and pseudo labeled images. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. You signed in with another tab or window. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Iterative training is not used here for simplicity. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. The architectures for the student and teacher models can be the same or different. But training robust supervised learning models is requires this step. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. Soft pseudo labels lead to better performance for low confidence data. putting back the student as the teacher. In the following, we will first describe experiment details to achieve our results. We present a simple self-training method that achieves 87.4 This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Chowdhury et al. The best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Please refer to [24] for details about mFR and AlexNets flip probability. Papers With Code is a free resource with all data licensed under. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. For instance, on ImageNet-A, Noisy Student achieves 74.2% top-1 accuracy which is approximately 57% more accurate than the previous state-of-the-art model. ; 2006)[book reviews], Semi-supervised deep learning with memory, Proceedings of the European Conference on Computer Vision (ECCV), Xception: deep learning with depthwise separable convolutions, K. Clark, M. Luong, C. D. Manning, and Q. V. Le, Semi-supervised sequence modeling with cross-view training, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, AutoAugment: learning augmentation strategies from data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, RandAugment: practical data augmentation with no separate search, Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, Good semi-supervised learning that requires a bad gan, T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, A. Galloway, A. Golubeva, T. Tanay, M. Moussa, and G. W. Taylor, R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow, I. J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and harnessing adversarial examples, Semi-supervised learning by entropy minimization, Advances in neural information processing systems, K. Gu, B. Yang, J. Ngiam, Q. When the student model is deliberately noised it is actually trained to be consistent to the more powerful teacher model that is not noised when it generates pseudo labels. . We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Self-training with Noisy Student improves ImageNet classification Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Self-training was previously used to improve ResNet-50 from 76.4% to 81.2% top-1 accuracy[76] which is still far from the state-of-the-art accuracy. It is expensive and must be done with great care. Self-Training With Noisy Student Improves ImageNet Classification This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. 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. Med. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images https://arxiv.org/abs/1911.04252, Accompanying notebook and sources to "A Guide to Pseudolabelling: How to get a Kaggle medal with only one model" (Dec. 2020 PyData Boston-Cambridge Keynote), Deep learning has shown remarkable successes in image recognition in recent years[35, 66, 62, 23, 69]. all 12, Image Classification It is experimentally validated that, for a target test resolution, using a lower train resolution offers better classification at test time, and a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ is proposed. Code for Noisy Student Training. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. As shown in Table2, Noisy Student with EfficientNet-L2 achieves 87.4% top-1 accuracy which is significantly better than the best previously reported accuracy on EfficientNet of 85.0%. Self-training with Noisy Student improves ImageNet classification. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Noisy Student Explained | Papers With Code This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. IEEE Transactions on Pattern Analysis and Machine Intelligence. Noise Self-training with Noisy Student 1. Here we study if it is possible to improve performance on small models by using a larger teacher model, since small models are useful when there are constraints for model size and latency in real-world applications. A novel random matrix theory based damping learner for second order optimisers inspired by linear shrinkage estimation is developed, and it is demonstrated that the derived method works well with adaptive gradient methods such as Adam. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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. 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. Self-training We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Self-Training for Natural Language Understanding! This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Edit social preview. For more information about the large architectures, please refer to Table7 in Appendix A.1. supervised model from 97.9% accuracy to 98.6% accuracy. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. 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, we iterate the algorithm a few times by treating the student as a teacher to generate new pseudo labels and train a new student. As noise injection methods are not used in the student model, and the student model was also small, it is more difficult to make the student better than teacher. When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single model. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and 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. Self-training with Noisy Student improves ImageNet classification Although noise may appear to be limited and uninteresting, when it is applied to unlabeled data, it has a compound benefit of enforcing local smoothness in the decision function on both labeled and unlabeled data. 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. There was a problem preparing your codespace, please try again. CVPR 2020 Open Access Repository et al. For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. This work proposes a novel architectural unit, which is term the Squeeze-and-Excitation (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and shows that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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]. 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. 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Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. tsai - Noisy student Self-training with noisy student improves imagenet classification. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. First, a teacher model is trained in a supervised fashion. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. Self-training with Noisy Student improves ImageNet classication Qizhe Xie 1, Minh-Thang Luong , Eduard Hovy2, Quoc V. Le1 1Google Research, Brain Team, 2Carnegie Mellon University fqizhex, thangluong, qvlg@google.com, hovy@cmu.edu Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . . Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen, GPipe: efficient training of giant neural networks using pipeline parallelism, A. Iscen, G. Tolias, Y. Avrithis, and O. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. Self-Training With Noisy Student Improves ImageNet Classification Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. Summarization_self-training_with_noisy_student_improves_imagenet We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. 10687-10698 Abstract A. Krizhevsky, I. Sutskever, and G. E. 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