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 a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. 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. (Submitted on 11 Nov 2019) We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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]). Especially unlabeled images are plentiful and can be collected with ease. 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. 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. This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. Self-Training With Noisy Student Improves ImageNet Classification 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. on ImageNet ReaL We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. We find that Noisy Student is better with an additional trick: data balancing. . If nothing happens, download Xcode and try again. Are labels required for improving adversarial robustness? Please refer to [24] for details about mFR and AlexNets flip probability. combination of labeled and pseudo labeled images. We used the version from [47], which filtered the validation set of ImageNet. Self-Training With Noisy Student Improves ImageNet Classification @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019 . We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. Here we study how to effectively use out-of-domain data. 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. For classes where we have too many images, we take the images with the highest confidence. We duplicate images in classes where there are not enough images. This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. However an important requirement for Noisy Student to work well is that the student model needs to be sufficiently large to fit more data (labeled and pseudo labeled). A tag already exists with the provided branch name. However, manually annotating organs from CT scans is time . Self-training with Noisy Student improves ImageNet classification In other words, the student is forced to mimic a more powerful ensemble model. Code for Noisy Student Training. 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. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. 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. For simplicity, we experiment with using 1128,164,132,116,14 of the whole data by uniformly sampling images from the the unlabeled set though taking the images with highest confidence leads to better results. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. ; 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. 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. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Self-Training With Noisy Student Improves ImageNet Classification Learn more. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. For instance, on the right column, as the image of the car undergone a small rotation, the standard model changes its prediction from racing car to car wheel to fire engine. Self-training with Noisy Student improves ImageNet classification The model with Noisy Student can successfully predict the correct labels of these highly difficult images. 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. 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. Compared to consistency training[45, 5, 74], the self-training / teacher-student framework is better suited for ImageNet because we can train a good teacher on ImageNet using label data. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. 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. Our work is based on self-training (e.g.,[59, 79, 56]). Noisy Student Training seeks to improve on self-training and distillation in two ways. On, International journal of molecular sciences. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. Finally, in the above, we say that the pseudo labels can be soft or hard. 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. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. LeLinks:YouTube: https://www.youtube.com/c/yannickilcherTwitter: https://twitter.com/ykilcherDiscord: https://discord.gg/4H8xxDFBitChute: https://www.bitchute.com/channel/yannic-kilcherMinds: https://www.minds.com/ykilcherParler: https://parler.com/profile/YannicKilcherLinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/If you want to support me, the best thing to do is to share out the content :)If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):SubscribeStar (preferred to Patreon): https://www.subscribestar.com/yannickilcherPatreon: https://www.patreon.com/yannickilcherBitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cqEthereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9mMonero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n unlabeled images. 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 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. The algorithm is basically self-training, a method in semi-supervised learning (. It implements SemiSupervised Learning with Noise to create an Image Classification. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. A semi-supervised segmentation network based on noisy student learning - : self-training_with_noisy_student_improves_imagenet_classification Please There was a problem preparing your codespace, please try again. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 2023.3.1_2 - It is expensive and must be done with great care. 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. Parthasarathi et al. . Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. We use the standard augmentation instead of RandAugment in this experiment. augmentation, dropout, stochastic depth to the student so that the noised 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. This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. This model investigates a new method. 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. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Self-training with Noisy Student improves ImageNet classification Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. ImageNet images and use it as a teacher to generate pseudo labels on 300M A tag already exists with the provided branch name. Self-training with Noisy Student improves ImageNet classification. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from Code is available at https://github.com/google-research/noisystudent. On robustness test sets, it improves Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. We iterate this process by putting back the student as the teacher. We use stochastic depth[29], dropout[63] and RandAugment[14]. Astrophysical Observatory. 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. 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]. task. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Learn more. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. CVPR 2020 Open Access Repository 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. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. labels, the teacher is not noised so that the pseudo labels are as good as 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 common workaround is to use entropy minimization or ramp up the consistency loss. These CVPR 2020 papers are the Open Access versions, provided by the. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 10687-10698 Abstract After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 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. 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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.