The inputs to the algorithm are both labeled and unlabeled 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. We sample 1.3M images in confidence intervals. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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. But during the learning of the student, we inject noise such as data Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. Self-Training : Noisy Student : Self-training This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. Self-training with Noisy Student improves ImageNet classification Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. 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. The comparison is shown in Table 9. 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. The main use case of knowledge distillation is model compression by making the student model smaller. combination of labeled and pseudo labeled images. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Code for Noisy Student Training. 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. student is forced to learn harder from the pseudo labels. ImageNet-A top-1 accuracy from 16.6 This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. . Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. The performance drops when we further reduce it. Train a classifier on labeled data (teacher). 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. In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. Our work is based on self-training (e.g.,[59, 79, 56]). Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. We iterate this process by putting back the student as the teacher. Work fast with our official CLI. Noisy Student Explained | Papers With Code This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. 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. 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. 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. However, manually annotating organs from CT scans is time . 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]. and surprising gains on robustness and adversarial benchmarks. 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 Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. International Conference on Machine Learning, Learning extraction patterns for subjective expressions, Proceedings of the 2003 conference on Empirical methods in natural language processing, A. Roy Chowdhury, P. Chakrabarty, A. Singh, S. Jin, H. Jiang, L. Cao, and E. G. Learned-Miller, Automatic adaptation of object detectors to new domains using self-training, T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, Probability of error of some adaptive pattern-recognition machines, W. Shi, Y. Gong, C. Ding, Z. MaXiaoyu Tao, and N. Zheng, Transductive semi-supervised deep learning using min-max features, C. Simon-Gabriel, Y. Ollivier, L. Bottou, B. Schlkopf, and D. Lopez-Paz, First-order adversarial vulnerability of neural networks and input dimension, Very deep convolutional networks for large-scale image recognition, N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. Self-training 1 2Self-training 3 4n What is Noisy Student? This model investigates a new method. We iterate this process by putting back the student as the teacher. Iterative training is not used here for simplicity. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. We will then show our results on ImageNet and compare them with state-of-the-art models. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Self-training with Noisy Student improves ImageNet classification. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. 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. Train a larger classifier on the combined set, adding noise (noisy student). 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. 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. On robustness test sets, it improves ImageNet-A top . 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. Unlike previous studies in semi-supervised learning that use in-domain unlabeled data (e.g, ., CIFAR-10 images as unlabeled data for a small CIFAR-10 training set), to improve ImageNet, we must use out-of-domain unlabeled data. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. These significant gains in robustness in ImageNet-C and ImageNet-P are surprising because our models were not deliberately optimizing for robustness (e.g., via data augmentation). 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. Especially unlabeled images are plentiful and can be collected with ease. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 3429-3440. . This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and We use the labeled images to train a teacher model using the standard cross entropy loss. Proceedings of the eleventh annual conference on Computational learning theory, Proceedings of the IEEE conference on computer vision and pattern recognition, Empirical Methods in Natural Language Processing (EMNLP), Imagenet classification with deep convolutional neural networks, Domain adaptive transfer learning with specialist models, Thirty-Second AAAI Conference on Artificial Intelligence, Regularized evolution for image classifier architecture search, Inception-v4, inception-resnet and the impact of residual connections on learning. , have shown that computer vision models lack robustness. A common workaround is to use entropy minimization or ramp up the consistency loss. Self-training with Noisy Student improves ImageNet classification 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. 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. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). In particular, we set the survival probability in stochastic depth to 0.8 for the final layer and follow the linear decay rule for other layers. SelfSelf-training with Noisy Student improves ImageNet classification 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. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. over the JFT dataset to predict a label for each image. Code is available at https://github.com/google-research/noisystudent. to noise the student. 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. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Their main goal is to find a small and fast model for deployment. 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 . Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . 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. CVPR 2020 Open Access Repository Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le Description: 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. The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. Self-Training With Noisy Student Improves ImageNet Classification Self-training with Noisy Student improves ImageNet classification possible. Finally, in the above, we say that the pseudo labels can be soft or hard. 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. On, International journal of molecular sciences. We also list EfficientNet-B7 as a reference. PDF Self-Training with Noisy Student Improves ImageNet Classification Self-training with Noisy Student improves ImageNet classification 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. 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. In other words, small changes in the input image can cause large changes to the predictions. 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. Different kinds of noise, however, may have different effects. But training robust supervised learning models is requires this step. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. ImageNet . Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10687-10698, (2020 . 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. 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. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. There was a problem preparing your codespace, please try again. For more information about the large architectures, please refer to Table7 in Appendix A.1. et al. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Especially unlabeled images are plentiful and can be collected with ease. Chowdhury et al. To achieve this result, we first train an EfficientNet model on labeled Self-training with Noisy Student improves ImageNet classification We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. 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. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. [68, 24, 55, 22]. 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. Infer labels on a much larger unlabeled dataset. 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. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. Hence we use soft pseudo labels for our experiments unless otherwise specified. IEEE Trans. We iterate this process by putting back the student as the teacher. Learn more. We iterate this process by putting back the student as the teacher. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. ImageNet images and use it as a teacher to generate pseudo labels on 300M 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. 10687-10698). Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial 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.
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self training with noisy student improves imagenet classification