For more information about the large architectures, please refer to Table7 in Appendix A.1. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. We use the same architecture for the teacher and the student and do not perform iterative training. Infer labels on a much larger unlabeled dataset. Different types of. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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 Self-training with Noisy Student improves ImageNet classification Original paper: https://arxiv.org/pdf/1911.04252.pdf Authors: Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le HOYA012 Introduction EfficientNet ImageNet SOTA EfficientNet First, we run an EfficientNet-B0 trained on ImageNet[69]. If nothing happens, download Xcode and try again. 27.8 to 16.1. We then select images that have confidence of the label higher than 0.3. 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]. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . . Finally, in the above, we say that the pseudo labels can be soft or hard. There was a problem preparing your codespace, please try again. 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. The algorithm is basically self-training, a method in semi-supervised learning (. The performance consistently drops with noise function removed. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. 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. 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 baseline model achieves an accuracy of 83.2. (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. unlabeled images. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Noisy Students performance improves with more unlabeled data. 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. . 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. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. Edit social preview. Please 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. Flip probability is the probability that the model changes top-1 prediction for different perturbations. 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. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. To date (2020) we will introduce "Noisy Student Training", which is a state-of-the-art model.The idea is to extend self-training and Distillation, a paper that shows that by adding three noises and distilling multiple times, the student model will have better generalization performance than 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. This invariance constraint reduces the degrees of freedom in the model. 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. Code is available at https://github.com/google-research/noisystudent. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Use, Smithsonian Are labels required for improving adversarial robustness? Finally, frameworks in semi-supervised learning also include graph-based methods [84, 73, 77, 33], methods that make use of latent variables as target variables [32, 42, 78] and methods based on low-density separation[21, 58, 15], which might provide complementary benefits to our method. to noise the student. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. 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. Self-training with Noisy Student. 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. 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. 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. 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. Train a larger classifier on the combined set, adding noise (noisy student). During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment. 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. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . It implements SemiSupervised Learning with Noise to create an Image Classification. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. We then use the teacher model to generate pseudo labels on unlabeled images. 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]. augmentation, dropout, stochastic depth to the student so that the noised 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. 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 . Imaging, 39 (11) (2020), pp. 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. 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. Please refer to [24] for details about mCE and AlexNets error rate. 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. Add a 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 experiments show that an important element for this simple method to work well at scale is that the student model should be noised during its training while the teacher should not be noised during the generation of pseudo labels. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. IEEE Transactions on Pattern Analysis and Machine Intelligence. To intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct. 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 . ImageNet-A top-1 accuracy from 16.6 10687-10698). 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 shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. Please The most interesting image is shown on the right of the first row. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. 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]. 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]. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 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. By clicking accept or continuing to use the site, you agree to the terms outlined in our. These CVPR 2020 papers are the Open Access versions, provided by the. 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. We use a resolution of 800x800 in this experiment. For classes where we have too many images, we take the images with the highest confidence. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 3.5B weakly labeled Instagram 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. Use Git or checkout with SVN using the web URL. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. You signed in with another tab or window. 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. 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Self-training with Noisy Student improves ImageNet classification Abstract. 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. 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. to use Codespaces. This material is presented to ensure timely dissemination of scholarly and technical work. 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. et al. [68, 24, 55, 22]. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. 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. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. over the JFT dataset to predict a label for each image. 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. 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. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. self-mentoring outperforms data augmentation and self training. On . Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. We then train a larger EfficientNet as a student model on the Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). 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. In this section, we study the importance of noise and the effect of several noise methods used in our model. Copyright and all rights therein are retained by authors or by other copyright holders. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Self-training with Noisy Student improves ImageNet classificationCVPR2020, Codehttps://github.com/google-research/noisystudent, Self-training, 1, 2Self-training, Self-trainingGoogleNoisy Student, Noisy Studentstudent modeldropout, stochastic depth andaugmentationteacher modelNoisy Noisy Student, Noisy Student, 1, JFT3ImageNetEfficientNet-B00.3130K130K, EfficientNetbaseline modelsEfficientNetresnet, EfficientNet-B7EfficientNet-L0L1L2, batchsize = 2048 51210242048EfficientNet-B4EfficientNet-L0l1L2350epoch700epoch, 2EfficientNet-B7EfficientNet-L0, 3EfficientNet-L0EfficientNet-L1L0, 4EfficientNet-L1EfficientNet-L2, student modelNoisy, noisystudent modelteacher modelNoisy, Noisy, Self-trainingaugmentationdropoutstochastic depth, Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores., 12/self-training-with-noisy-student-f33640edbab2, EfficientNet-L0EfficientNet-B7B7, EfficientNet-L1EfficientNet-L0, EfficientNetsEfficientNet-L1EfficientNet-L2EfficientNet-L2EfficientNet-B75. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. We iterate this process by putting back the student as the teacher. and surprising gains on robustness and adversarial benchmarks. 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. A common workaround is to use entropy minimization or ramp up the consistency loss. 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. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. 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. We use the labeled images to train a teacher model using the standard cross entropy loss. 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. A tag already exists with the provided branch name. The width. For RandAugment, we apply two random operations with the magnitude set to 27. Image Classification 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. Infer labels on a much larger unlabeled dataset. 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. 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. Similar to[71], we fix the shallow layers during finetuning. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. In particular, we first perform normal training with a smaller resolution for 350 epochs. In the following, we will first describe experiment details to achieve our results. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). We apply dropout to the final classification layer with a dropout rate of 0.5. 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. 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. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. unlabeled images , . 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. Noisy Student self-training is an effective way to leverage unlabelled datasets and improving accuracy by adding noise to the student model while training so it learns beyond the teacher's knowledge. labels, the teacher is not noised so that the pseudo labels are as good as We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled images. Semi-supervised medical image classification with relation-driven self-ensembling model. - : self-training_with_noisy_student_improves_imagenet_classification student is forced to learn harder from the pseudo labels. In contrast, the predictions of the model with Noisy Student remain quite stable. Self-training with noisy student improves imagenet classification. 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 . We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. Test images on ImageNet-P underwent different scales of perturbations. 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.