self training with noisy student improves imagenet classification

Self-Training With Noisy Student Improves ImageNet Classification corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from In other words, the student is forced to mimic a more powerful ensemble model. But training robust supervised learning models is requires this step. 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. Train a classifier on labeled data (teacher). For labeled images, we use a batch size of 2048 by default and reduce the batch size when we could not fit the model into the memory. Here we show the evidence in Table 6, noise such as stochastic depth, dropout and data augmentation plays an important role in enabling the student model to perform better than the teacher. For each class, we select at most 130K images that have the highest confidence. Please After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 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. Self-training with Noisy Student improves ImageNet classification. For more information about the large architectures, please refer to Table7 in Appendix A.1. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Self-training with Noisy Student improves ImageNet classification 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]. If nothing happens, download GitHub Desktop and try again. Zoph et al. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. Self-training is a form of semi-supervised learning [10] which attempts to leverage unlabeled data to improve classification performance in the limited data regime. If nothing happens, download GitHub Desktop and try again. Noisy Student Explained | Papers With Code 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. We use a resolution of 800x800 in this experiment. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. Self-mentoring: : A new deep learning pipeline to train a self SelfSelf-training with Noisy Student improves ImageNet classification For example, with all noise removed, the accuracy drops from 84.9% to 84.3% in the case with 130M unlabeled images and drops from 83.9% to 83.2% in the case with 1.3M unlabeled images. on ImageNet, which is 1.0 Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. putting back the student as the teacher. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. 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. PDF Self-Training with Noisy Student Improves ImageNet Classification Here we use unlabeled images to improve the state-of-the-art ImageNet accuracy and show that the accuracy gain has an outsized impact on robustness. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Self-training with Noisy Student improves ImageNet classification. ImageNet-A top-1 accuracy from 16.6 Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. to use Codespaces. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. Learn more. Flip probability is the probability that the model changes top-1 prediction for different perturbations. We then perform data filtering and balancing on this corpus. Self-training with Noisy Student improves ImageNet classification Work fast with our official CLI. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. 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. In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. Code for Noisy Student Training. self-mentoring outperforms data augmentation and self training. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. to use Codespaces. Please refer to [24] for details about mFR and AlexNets flip probability. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. 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. Self-training with Noisy Student improves ImageNet classification Self-Training With Noisy Student Improves ImageNet Classification IEEE Trans. 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. 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%. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. . As can be seen from Table 8, the performance stays similar when we reduce the data to 116 of the total data, which amounts to 8.1M images after duplicating. Computer Science - Computer Vision and Pattern Recognition. We iterate this process by putting back the student as the teacher. 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. Specifically, as all classes in ImageNet have a similar number of labeled images, we also need to balance the number of unlabeled images for each class. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. There was a problem preparing your codespace, please try again. 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. This model investigates a new method. As can be seen from the figure, our model with Noisy Student makes correct predictions for images under severe corruptions and perturbations such as snow, motion blur and fog, while the model without Noisy Student suffers greatly under these conditions. For RandAugment, we apply two random operations with the magnitude set to 27. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. The main difference between our work and prior works is that we identify the importance of noise, and aggressively inject noise to make the student better. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Especially unlabeled images are plentiful and can be collected with ease. 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. 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 the following, we will first describe experiment details to achieve our results. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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. It 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. augmentation, dropout, stochastic depth to the student so that the noised Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. Self-training with Noisy Student improves ImageNet classification 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. We do not tune these hyperparameters extensively since our method is highly robust to them. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Code is available at https://github.com/google-research/noisystudent. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Astrophysical Observatory. 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. 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. 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. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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. 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. Due to duplications, there are only 81M unique images among these 130M images. Note that these adversarial robustness results are not directly comparable to prior works since we use a large input resolution of 800x800 and adversarial vulnerability can scale with the input dimension[17, 20, 19, 61]. 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. Edit social preview. Self-Training with Noisy Student Improves ImageNet Classification Please 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. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Finally, in the above, we say that the pseudo labels can be soft or hard. To achieve this result, we first train an EfficientNet model on labeled et al. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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