self training with noisy student improves imagenet classification self training with noisy student improves imagenet classification

It implements SemiSupervised Learning with Noise to create an Image Classification. The best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. The width. Please 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. We then perform data filtering and balancing on this corpus. on ImageNet ReaL 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. Parthasarathi et al. 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). . . We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. This is probably because it is harder to overfit the large unlabeled dataset. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. to use Codespaces. 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. ; 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. Then, that teacher is used to label the unlabeled data. We improved it by adding noise to the student to learn beyond the teachers knowledge. 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. On robustness test sets, it improves ImageNet-A top . The paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task is revisited, and a simple recipe that is called Big Transfer (BiT) is created, which achieves strong performance on over 20 datasets. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. 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. Their main goal is to find a small and fast model for deployment. 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. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 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 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. Self-training with Noisy Student improves ImageNet classification Abstract. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. 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 found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Our procedure went as follows. Edit social preview. Self-Training Noisy Student " " Self-Training . Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. The main use case of knowledge distillation is model compression by making the student model smaller. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. 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. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Semi-supervised medical image classification with relation-driven self-ensembling model. 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. and surprising gains on robustness and adversarial benchmarks. This material is presented to ensure timely dissemination of scholarly and technical work. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Especially unlabeled images are plentiful and can be collected with ease. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. This invariance constraint reduces the degrees of freedom in the model. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Significantly, after using the masks generated by student-SN, the classification performance improved by 0.9 of AC, 0.7 of SE, and 0.9 of AUC. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. On, International journal of molecular sciences. , have shown that computer vision models lack robustness. First, a teacher model is trained in a supervised fashion. 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. 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. At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. The most interesting image is shown on the right of the first row. 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. 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. But during the learning of the student, we inject noise such as data 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. It is experimentally validated that, for a target test resolution, using a lower train resolution offers better classification at test time, and a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ is proposed. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Their noise model is video specific and not relevant for image classification. With Noisy Student, the model correctly predicts dragonfly for the image. The comparison is shown in Table 9. 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. 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. We use the labeled images to train a teacher model using the standard cross entropy loss. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. ImageNet-A top-1 accuracy from 16.6 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. We use the standard augmentation instead of RandAugment in this experiment. 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 . Train a larger classifier on the combined set, adding noise (noisy student). We also study the effects of using different amounts of unlabeled data. We then train a larger EfficientNet as a student model on the As can be seen from the figure, our model with Noisy Student makes correct predictions for images under severe corruptions and perturbations such as snow, motion blur and fog, while the model without Noisy Student suffers greatly under these conditions. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative 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 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. 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. 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. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. 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 paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. Self-training with Noisy Student. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . labels, the teacher is not noised so that the pseudo labels are as good as Agreement NNX16AC86A, Is ADS down? This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Here we show the evidence in Table 6, noise such as stochastic depth, dropout and data augmentation plays an important role in enabling the student model to perform better than the teacher. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. Use Git or checkout with SVN using the web URL. 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 improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. 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]. Are you sure you want to create this branch? 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. 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Aggarwal, Y. Huang, and Q. V. Le, Proceedings of the AAAI Conference on Artificial Intelligence, B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. For RandAugment, we apply two random operations with the magnitude set to 27. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Noisy Students performance improves with more unlabeled data. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. 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]. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. Image Classification The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Test images on ImageNet-P underwent different scales of perturbations. 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. However, manually annotating organs from CT scans is time . Similar to[71], we fix the shallow layers during finetuning. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Code for Noisy Student Training. Learn more. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. . Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Code is available at https://github.com/google-research/noisystudent. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. unlabeled images. We use the same architecture for the teacher and the student and do not perform iterative training. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Hence we use soft pseudo labels for our experiments unless otherwise specified. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. 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. Imaging, 39 (11) (2020), pp. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. These CVPR 2020 papers are the Open Access versions, provided by the. We iterate this process by putting back the student as the teacher. The performance drops when we further reduce it. Work fast with our official CLI. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. We use stochastic depth[29], dropout[63] and RandAugment[14]. If nothing happens, download GitHub Desktop and try again. We use a resolution of 800x800 in this experiment. Next, a larger student model is trained on the combination of all data and achieves better performance than the teacher by itself.OUTLINE:0:00 - Intro \u0026 Overview1:05 - Semi-Supervised \u0026 Transfer Learning5:45 - Self-Training \u0026 Knowledge Distillation10:00 - Noisy Student Algorithm Overview20:20 - Noise Methods22:30 - Dataset Balancing25:20 - Results30:15 - Perturbation Robustness34:35 - Ablation Studies39:30 - Conclusion \u0026 CommentsPaper: https://arxiv.org/abs/1911.04252Code: https://github.com/google-research/noisystudentModels: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnetAbstract:We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. If nothing happens, download GitHub Desktop and try again. In terms of methodology, 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. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and . There was a problem preparing your codespace, please try again. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. unlabeled images , . Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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Our work is based on self-training (e.g.,[59, 79, 56]). 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.