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. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 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). Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. Self-training with Noisy Student improves ImageNet classification Abstract. 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. 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]. 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. Are labels required for improving adversarial robustness? The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. 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. Abdominal organ segmentation is very important for clinical applications. 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. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. In the following, we will first describe experiment details to achieve our results. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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[2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . 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. Work fast with our official CLI. We find that Noisy Student is better with an additional trick: data balancing. Notice, Smithsonian Terms of Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. 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. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Are you sure you want to create this branch? We also list EfficientNet-B7 as a reference. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. 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. 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. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. 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. 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. But training robust supervised learning models is requires this step. sign in Noisy Student can still improve the accuracy to 1.6%. 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. 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. Edit social preview. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The width. The performance consistently drops with noise function removed. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. Use Git or checkout with SVN using the web URL. (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. 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]. 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. 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. augmentation, dropout, stochastic depth to the student so that the noised 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. et al. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . on ImageNet ReaL. 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. On . supervised model from 97.9% accuracy to 98.6% accuracy. Their purpose is different from ours: to adapt a teacher model on one domain to another. We use the standard augmentation instead of RandAugment in this experiment. The baseline model achieves an accuracy of 83.2. 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 other words, small changes in the input image can cause large changes to the predictions. You signed in with another tab or window. 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]. A number of studies, e.g. 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. student is forced to learn harder from the pseudo labels. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. We use stochastic depth[29], dropout[63] and RandAugment[14]. self-mentoring outperforms data augmentation and self training. 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. However, manually annotating organs from CT scans is time . Classification of Socio-Political Event Data, SLADE: A Self-Training Framework For Distance Metric Learning, Self-Training with Differentiable Teacher, https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. to use Codespaces. First, a teacher model is trained in a supervised fashion. 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. Their noise model is video specific and not relevant for image classification. 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). 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. We use EfficientNet-B4 as both the teacher and the student. For more information about the large architectures, please refer to Table7 in Appendix A.1. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 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. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. putting back the student as the teacher. Chowdhury et al. Code is available at https://github.com/google-research/noisystudent. 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. 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. Train a larger classifier on the combined set, adding noise (noisy student). Further, Noisy Student outperforms the state-of-the-art accuracy of 86.4% by FixRes ResNeXt-101 WSL[44, 71] that requires 3.5 Billion Instagram images labeled with tags. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Papers With Code is a free resource with all data licensed under. We duplicate images in classes where there are not enough images. 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. [68, 24, 55, 22]. task. and surprising gains on robustness and adversarial benchmarks. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. 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-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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. on ImageNet, which is 1.0 Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical 10687-10698). 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. 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. These CVPR 2020 papers are the Open Access versions, provided by the. 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. all 12, Image Classification After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. A tag already exists with the provided branch name. 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. Copyright and all rights therein are retained by authors or by other copyright holders. 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. 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. (using extra training data). We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. We iterate this process by putting back the student as the teacher. 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. Code for Noisy Student Training. We improved it by adding noise to the student to learn beyond the teachers knowledge. We use a resolution of 800x800 in this experiment. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. 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%. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . 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. Their main goal is to find a small and fast model for deployment. For classes where we have too many images, we take the images with the highest confidence. 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. The inputs to the algorithm are both labeled and unlabeled images. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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. 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. . Noisy Students performance improves with more unlabeled data. Different types of. As can be seen from Table 8, the performance stays similar when we reduce the data to 116 of the total data, which amounts to 8.1M images after duplicating. On robustness test sets, it improves The abundance of data on the internet is vast. In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. on ImageNet ReaL Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Our main results are shown in Table1.

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