10687-10698). Self-training with Noisy Student improves ImageNet classification Self-Training With Noisy Student Improves ImageNet Classification Abstract: We present a simple self-training method that 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. By clicking accept or continuing to use the site, you agree to the terms outlined in our. You signed in with another tab or window. 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. Self-Training With Noisy Student Improves ImageNet Classification "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. We use a resolution of 800x800 in this experiment. Learn more. Self-training with Noisy Student improves ImageNet classification In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. 3429-3440. . Especially unlabeled images are plentiful and can be collected with ease. The performance drops when we further reduce it. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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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. We also study the effects of using different amounts of unlabeled data. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Their main goal is to find a small and fast model for deployment. We apply dropout to the final classification layer with a dropout rate of 0.5. 3.5B weakly labeled Instagram images. We present a simple self-training method that achieves 87.4 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. . ; 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. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. However, 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. We use the labeled images to train a teacher model using the standard cross entropy loss. There was a problem preparing your codespace, please try again. Notice, Smithsonian Terms of This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. Are you sure you want to create this branch? and surprising gains on robustness and adversarial benchmarks. For more information about the large architectures, please refer to Table7 in Appendix A.1. This is probably because it is harder to overfit the large unlabeled dataset. Self-training with Noisy Student improves ImageNet classification Abstract. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images augmentation, dropout, stochastic depth to the student so that the noised This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Models are available at this https URL. Why Self-training with Noisy Students beats SOTA Image classification 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. Le. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. However, 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. As shown in Table3,4 and5, when compared with the previous state-of-the-art model ResNeXt-101 WSL[44, 48] trained on 3.5B weakly labeled images, Noisy Student yields substantial gains on robustness datasets. Self-training Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Noise Self-training with Noisy Student 1. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Noisy Student Training is a semi-supervised learning approach. We use the standard augmentation instead of RandAugment in this experiment. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. The width. We iterate this process by On . self-mentoring outperforms data augmentation and self training. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . We first report the validation set accuracy on the ImageNet 2012 ILSVRC challenge prediction task as commonly done in literature[35, 66, 23, 69] (see also [55]). Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. Please to use Codespaces. Hence we use soft pseudo labels for our experiments unless otherwise specified. 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. In the following, we will first describe experiment details to achieve our results. Self-training with Noisy Student improves ImageNet classification Since we use soft pseudo labels generated from the teacher model, when the student is trained to be exactly the same as the teacher model, the cross entropy loss on unlabeled data would be zero and the training signal would vanish. As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Self-training with Noisy Student. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. team using this approach not only surpasses the top-1 ImageNet accuracy of SOTA models by 1%, it also shows that the robustness of a model also improves. supervised model from 97.9% accuracy to 98.6% accuracy. The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. We determine number of training steps and the learning rate schedule by the batch size for labeled images. The main use case of knowledge distillation is model compression by making the student model smaller. We present a simple self-training method that 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. . 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. 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. The ONCE (One millioN sCenEs) dataset for 3D object detection in the autonomous driving scenario is introduced and a benchmark is provided in which a variety of self-supervised and semi- supervised methods on the ONCE dataset are evaluated. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). 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. We use EfficientNet-B4 as both the teacher and the student. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results.