self training with noisy student improves imagenet classification

Code is available at https://github.com/google-research/noisystudent. The most interesting image is shown on the right of the first row. Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. self-mentoring outperforms data augmentation and self training. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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. Different types of. Our procedure went as follows. We use EfficientNet-B0 as both the teacher model and the student model and compare using Noisy Student with soft pseudo labels and hard pseudo labels. The main use case of knowledge distillation is model compression by making the student model smaller. 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. Their noise model is video specific and not relevant for image classification. Use Git or checkout with SVN using the web URL. 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. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. The results are shown in Figure 4 with the following observations: (1) Soft pseudo labels and hard pseudo labels can both lead to great improvements with in-domain unlabeled images i.e., high-confidence images. 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. 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. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. By showing the models only labeled images, we limit ourselves from making use of unlabeled images available in much larger quantities to improve accuracy and robustness of state-of-the-art models. sign in The model with Noisy Student can successfully predict the correct labels of these highly difficult images. 3429-3440. . A number of studies, e.g. unlabeled images. We duplicate images in classes where there are not enough images. Use, Smithsonian Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Computer Science - Computer Vision and Pattern Recognition. Infer labels on a much larger unlabeled dataset. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. A tag already exists with the provided branch name. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. The results also confirm that vision models can benefit from Noisy Student even without iterative training. Self-training with Noisy Student improves ImageNet classification. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. 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 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. To achieve this result, we first train an EfficientNet model on labeled Noise Self-training with Noisy Student 1. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. ImageNet . The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. Agreement NNX16AC86A, Is ADS down? CLIP (Contrastive Language-Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.The idea of zero-data learning dates back over a decade [^reference-8] but until recently was mostly studied in computer vision as a way of generalizing to unseen object categories. [^reference-9] [^reference-10] A critical insight was to . We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. You signed in with another tab or window. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Noisy Students performance improves with more unlabeled data. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . In this section, we study the importance of noise and the effect of several noise methods used in our model. 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. 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. 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. Copyright and all rights therein are retained by authors or by other copyright holders. Noisy Student (B7, L2) means to use EfficientNet-B7 as the student and use our best model with 87.4% accuracy as the teacher model. 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. On . Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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. Do imagenet classifiers generalize to imagenet? 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]. Learn more. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. In typical self-training with the teacher-student framework, noise injection to the student is not used by default, or the role of noise is not fully understood or justified. 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. If nothing happens, download GitHub Desktop and try again. We iterate this process by putting back the student as the teacher. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. 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 top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Iterative training is not used here for simplicity. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. The architectures for the student and teacher models can be the same or different. Test images on ImageNet-P underwent different scales of perturbations. The abundance of data on the internet is vast. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. . . However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. 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. 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. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: For ImageNet checkpoints trained by Noisy Student Training, please refer to the EfficientNet github. A. 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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. 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. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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Then, that teacher is used to label the unlabeled data. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. We evaluate the best model, that achieves 87.4% top-1 accuracy, on three robustness test sets: ImageNet-A, ImageNet-C and ImageNet-P. ImageNet-C and P test sets[24] include images with common corruptions and perturbations such as blurring, fogging, rotation and scaling. 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. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Self-training with Noisy Student. 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%. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. 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. . (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. (using extra training data). 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 used the version from [47], which filtered the validation set of ImageNet. all 12, Image Classification Noisy Student Training is a semi-supervised learning method which achieves 88.4% top-1 accuracy on ImageNet (SOTA) and surprising gains on robustness and adversarial benchmarks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. . to use Codespaces. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation.