supervised model from 97.9% accuracy to 98.6% accuracy. Test images on ImageNet-P underwent different scales of perturbations. Chowdhury et al. 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. 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. 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. 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. 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 the labeled images to train a teacher model using the standard cross entropy loss. On, International journal of molecular sciences. 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. 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. Different types of. Our experiments show that an important element for this simple method to work well at scale is that the student model should be noised during its training while the teacher should not be noised during the generation of pseudo labels. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled images. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. These CVPR 2020 papers are the Open Access versions, provided by the. Soft pseudo labels lead to better performance for low confidence data. This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. 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. Papers With Code is a free resource with all data licensed under. 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. We also study the effects of using different amounts of unlabeled data. Different kinds of noise, however, may have different effects. A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. But training robust supervised learning models is requires this step. You signed in with another tab or window. 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. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. Self-Training With Noisy Student Improves ImageNet Classification Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. Astrophysical Observatory. This material is presented to ensure timely dissemination of scholarly and technical work. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. We present a simple self-training method that achieves 87.4 Although they have produced promising results, in our preliminary experiments, consistency regularization works less well on ImageNet because consistency regularization in the early phase of ImageNet training regularizes the model towards high entropy predictions, and prevents it from achieving good accuracy. 27.8 to 16.1. For each class, we select at most 130K images that have the highest confidence. Self-training with Noisy Student improves ImageNet classification Abstract. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. (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. The main difference between Data Distillation and our method is that we use the noise to weaken the student, which is the opposite of their approach of strengthening the teacher by ensembling. combination of labeled and pseudo labeled images. 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. Self-Training Noisy Student " " Self-Training . 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. Code is available at https://github.com/google-research/noisystudent. We iterate this process by putting back the student as the teacher. 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. Work fast with our official CLI. 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. Imaging, 39 (11) (2020), pp. We thank the Google Brain team, Zihang Dai, Jeff Dean, Hieu Pham, Colin Raffel, Ilya Sutskever and Mingxing Tan for insightful discussions, Cihang Xie for robustness evaluation, Guokun Lai, Jiquan Ngiam, Jiateng Xie and Adams Wei Yu for feedbacks on the draft, Yanping Huang and Sameer Kumar for improving TPU implementation, Ekin Dogus Cubuk and Barret Zoph for help with RandAugment, Yanan Bao, Zheyun Feng and Daiyi Peng for help with the JFT dataset, Olga Wichrowska and Ola Spyra for help with infrastructure. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. In this section, we study the importance of noise and the effect of several noise methods used in our model. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. We do not tune these hyperparameters extensively since our method is highly robust to them. In particular, we first perform normal training with a smaller resolution for 350 epochs. 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). 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. 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]). The performance drops when we further reduce it. The mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. Use Git or checkout with SVN using the web URL. For example, without Noisy Student, the model predicts bullfrog for the image shown on the left of the second row, which might be resulted from the black lotus leaf on the water. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. Zoph et al. We then use the teacher model to generate pseudo labels on unlabeled images. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Noisy Student can still improve the accuracy to 1.6%. 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 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. To achieve this result, we first train an EfficientNet model on labeled Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. Noisy Student Training is a semi-supervised learning approach. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. Please refer to [24] for details about mCE and AlexNets error rate. Learn more. 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. Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). This is probably because it is harder to overfit the large unlabeled dataset. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. ImageNet . Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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. 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. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 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. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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%. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. Due to duplications, there are only 81M unique images among these 130M images. to use Codespaces. 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. There was a problem preparing your codespace, please try again. Use, Smithsonian This paper reviews the state-of-the-art in both the field of CNNs for image classification and object detection and Autonomous Driving Systems (ADSs) in a synergetic way including a comprehensive trade-off analysis from a human-machine perspective. 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 After using the masks generated by teacher-SN, the classification performance improved by 0.2 of AC, 1.2 of SP, and 0.7 of AUC. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Our study shows that using unlabeled data improves accuracy and general robustness. We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. We use the standard augmentation instead of RandAugment in this experiment. 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. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. In the following, we will first describe experiment details to achieve our results. Are you sure you want to create this branch? As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. The main difference between our method and knowledge distillation is that knowledge distillation does not consider unlabeled data and does not aim to improve the student model. Self-training with Noisy Student improves ImageNet classification Original paper: https://arxiv.org/pdf/1911.04252.pdf Authors: Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le HOYA012 Introduction EfficientNet ImageNet SOTA EfficientNet
