self training with noisy student improves imagenet classification

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. If nothing happens, download GitHub Desktop and try again. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. 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. - : self-training_with_noisy_student_improves_imagenet_classification Works based on pseudo label[37, 31, 60, 1] are similar to self-training, but also suffers the same problem with consistency training, since it relies on a model being trained instead of a converged model with high accuracy to generate pseudo labels. to use Codespaces. 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. 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. 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]. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. The performance drops when we further reduce it. Self-training with Noisy Student improves ImageNet classification. 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. 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. (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. 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. to use Codespaces. 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]). 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. Chowdhury et al. 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 stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. For each class, we select at most 130K images that have the highest confidence. IEEE Transactions on Pattern Analysis and Machine Intelligence. Copyright and all rights therein are retained by authors or by other copyright holders. Are labels required for improving adversarial robustness? During this process, we kept increasing the size of the student model to improve the performance. It can be seen that masks are useful in improving classification performance. 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. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. We then select images that have confidence of the label higher than 0.3. We improved it by adding noise to the student to learn beyond the teachers knowledge. Edit social preview. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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Please Test images on ImageNet-P underwent different scales of perturbations. For RandAugment, we apply two random operations with the magnitude set to 27. But during the learning of the student, we inject noise such as 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. https://arxiv.org/abs/1911.04252. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. If nothing happens, download Xcode and try again. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. In other words, small changes in the input image can cause large changes to the predictions. 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. Models are available at this https URL. This work proposes a novel architectural unit, which is term the Squeeze-and-Excitation (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and shows that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. However, in the case with 130M unlabeled images, with noise function removed, the performance is still improved to 84.3% from 84.0% when compared to the supervised baseline. Self-training with Noisy Student improves ImageNet classification. Image Classification Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. task. Train a classifier on labeled data (teacher). Due to duplications, there are only 81M unique images among these 130M images. . Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. In terms of methodology, We find that using a batch size of 512, 1024, and 2048 leads to the same performance. Agreement NNX16AC86A, Is ADS down? 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. 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. 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. Soft pseudo labels lead to better performance for low confidence data. (or is it just me), Smithsonian Privacy But training robust supervised learning models is requires this step. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . 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. Self-training 1 2Self-training 3 4n What is Noisy Student? 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. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. https://arxiv.org/abs/1911.04252, Accompanying notebook and sources to "A Guide to Pseudolabelling: How to get a Kaggle medal with only one model" (Dec. 2020 PyData Boston-Cambridge Keynote), Deep learning has shown remarkable successes in image recognition in recent years[35, 66, 62, 23, 69]. Code is available at https://github.com/google-research/noisystudent. 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. supervised model from 97.9% accuracy to 98.6% accuracy. 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 performance consistently drops with noise function removed. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . For unlabeled images, we set the batch size to be three times the batch size of labeled images for large models, including EfficientNet-B7, L0, L1 and L2. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. We determine number of training steps and the learning rate schedule by the batch size for labeled images. Different types of. 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. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. ImageNet . 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. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. 3429-3440. . 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. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Astrophysical Observatory. Please refer to [24] for details about mCE and AlexNets error rate. Noisy Student can still improve the accuracy to 1.6%. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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. Add a The baseline model achieves an accuracy of 83.2. 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. The abundance of data on the internet is vast. 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. 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. However, manually annotating organs from CT scans is time . We iterate this process by putting back the student as the teacher. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Infer labels on a much larger unlabeled dataset. For classes where we have too many images, we take the images with the highest confidence. sign in Here we study how to effectively use out-of-domain data. We also study the effects of using different amounts of unlabeled data. Not only our method improves standard ImageNet accuracy, it also improves classification robustness on much harder test sets by large margins: ImageNet-A[25] top-1 accuracy from 16.6% to 74.2%, ImageNet-C[24] mean corruption error (mCE) from 45.7 to 31.2 and ImageNet-P[24] mean flip rate (mFR) from 27.8 to 16.1. Use Git or checkout with SVN using the web URL. These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. You signed in with another tab or window. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. 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. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Learn more. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Noisy StudentImageNetEfficientNet-L2state-of-the-art. Self-training with noisy student improves imagenet classification. With Noisy Student, the model correctly predicts dragonfly for the image. The algorithm is basically self-training, a method in semi-supervised learning (. Finally, in the above, we say that the pseudo labels can be soft or hard. We iterate this process by putting back the student as the teacher. We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. 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. 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. We then train a larger EfficientNet as a student model on the Use Git or checkout with SVN using the web URL. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Yalniz et al. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. We will then show our results on ImageNet and compare them with state-of-the-art models. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. 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]. Computer Science - Computer Vision and Pattern Recognition. The main use case of knowledge distillation is model compression by making the student model smaller. self-mentoring outperforms data augmentation and self training. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Le. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. On robustness test sets, it improves Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. We iterate this process by Ranked #14 on 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. . A tag already exists with the provided branch name. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. 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. Their main goal is to find a small and fast model for deployment. In contrast, the predictions of the model with Noisy Student remain quite stable. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Noisy Student Training seeks to improve on self-training and distillation in two ways. student is forced to learn harder from the pseudo labels. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. [^reference-9] [^reference-10] A critical insight was to . Work fast with our official CLI. 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. on ImageNet, which is 1.0 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. Code for Noisy Student Training. The most interesting image is shown on the right of the first row. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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. This is probably because it is harder to overfit the large unlabeled dataset. Self-Training Noisy Student " " Self-Training . Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Whether the model benefits from more unlabeled data depends on the capacity of the model since a small model can easily saturate, while a larger model can benefit from more data. 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]. A. Alemi, Thirty-First AAAI Conference on Artificial Intelligence, C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, EfficientNet: rethinking model scaling for convolutional neural networks, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, A. Vedaldi, M. Douze, and H. Jgou, Fixing the train-test resolution discrepancy, V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), J. Weston, F. Ratle, H. Mobahi, and R. Collobert, Deep learning via semi-supervised embedding, Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le, Unsupervised data augmentation for consistency training, S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, I. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . The proposed use of distillation to only handle easy instances allows for a more aggressive trade-off in the student size, thereby reducing the amortized cost of inference and achieving better accuracy than standard distillation. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. A number of studies, e.g. 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. 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]. We also list EfficientNet-B7 as a reference. 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. We iterate this process by putting back the student as the teacher. 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. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a [57] used self-training for domain adaptation. . 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. 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. It implements SemiSupervised Learning with Noise to create an Image Classification. The results also confirm that vision models can benefit from Noisy Student even without iterative training. The accuracy is improved by about 10% in most settings. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images 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. 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. 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. For instance, on ImageNet-A, Noisy Student achieves 74.2% top-1 accuracy which is approximately 57% more accurate than the previous state-of-the-art model. 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. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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.

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