As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. In the following, we will first describe experiment details to achieve our results. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. augmentation, dropout, stochastic depth to the student so that the noised The algorithm is basically self-training, a method in semi-supervised learning (. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. 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. We use EfficientNet-B4 as both the teacher and the student. Code is available at https://github.com/google-research/noisystudent. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. The baseline model achieves an accuracy of 83.2. The width. Do better imagenet models transfer better? 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. 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. 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. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. ImageNet . For RandAugment, we apply two random operations with the magnitude set to 27. et al. We used the version from [47], which filtered the validation set of ImageNet. . The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Agreement NNX16AC86A, Is ADS down? w Summary of key results compared to previous state-of-the-art models. A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet. Notice, Smithsonian Terms of 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. In other words, the student is forced to mimic a more powerful ensemble model. There was a problem preparing your codespace, please try again. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. As shown in Table2, Noisy Student with EfficientNet-L2 achieves 87.4% top-1 accuracy which is significantly better than the best previously reported accuracy on EfficientNet of 85.0%. unlabeled images , . This is probably because it is harder to overfit the large unlabeled dataset. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. We iterate this process by putting back the student as the teacher. Noisy StudentImageNetEfficientNet-L2state-of-the-art. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. On robustness test sets, it improves Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. We 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. 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. 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. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Please refer to [24] for details about mCE and AlexNets error rate. On robustness test sets, it improves ImageNet-A top . A tag already exists with the provided branch name. Yalniz et al. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. The accuracy is improved by about 10% in most settings. But during the learning of the student, we inject noise such as data This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). to use Codespaces. . 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. [57] used self-training for domain adaptation. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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 . Work fast with our official CLI. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. 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. These CVPR 2020 papers are the Open Access versions, provided by the. . These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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 . Summarization_self-training_with_noisy_student_improves_imagenet_classification. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Code for Noisy Student Training. This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. Imaging, 39 (11) (2020), pp. You signed in with another tab or window. 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. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. You signed in with another tab or window. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. Noisy Student can still improve the accuracy to 1.6%. 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. Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le Description: We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. , have shown that computer vision models lack robustness. 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. Train a larger classifier on the combined set, adding noise (noisy student). 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. 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 However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. over the JFT dataset to predict a label for each image. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). self-mentoring outperforms data augmentation and self training. We apply dropout to the final classification layer with a dropout rate of 0.5. Infer labels on a much larger unlabeled dataset. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Please refer to [24] for details about mFR and AlexNets flip probability. The abundance of data on the internet is vast. 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. We iterate this process by We iterate this process by putting back the student as the teacher. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 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. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. 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. 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. 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. Although noise may appear to be limited and uninteresting, when it is applied to unlabeled data, it has a compound benefit of enforcing local smoothness in the decision function on both labeled and unlabeled data. 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. 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. Infer labels on a much larger unlabeled dataset. 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. We then train a larger EfficientNet as a student model on the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). We also study the effects of using different amounts of unlabeled 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. 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. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Here we study how to effectively use out-of-domain data. 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. This material is presented to ensure timely dissemination of scholarly and technical work. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. A tag already exists with the provided branch name. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Noisy Student Training is a semi-supervised learning approach. This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. student is forced to learn harder from the pseudo labels. Different kinds of noise, however, may have different effects. 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]. Iterative training is not used here for simplicity. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. 10687-10698). The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. In terms of methodology, 3429-3440. . The comparison is shown in Table 9. Especially unlabeled images are plentiful and can be collected with ease. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. 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 Med. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative sign in 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]. 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. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. We use the same architecture for the teacher and the student and do not perform iterative training. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 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. 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. 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. 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. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. https://arxiv.org/abs/1911.04252. 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. to use Codespaces. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. 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. unlabeled images. By clicking accept or continuing to use the site, you agree to the terms outlined in our. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Learn more. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. With Noisy Student, the model correctly predicts dragonfly for the image. 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. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. on ImageNet ReaL Chowdhury et al. Noisy Student Training seeks to improve on self-training and distillation in two ways. combination of labeled and pseudo labeled images. The architectures for the student and teacher models can be the same or different. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. IEEE Transactions on Pattern Analysis and Machine Intelligence. (or is it just me), Smithsonian Privacy Train a classifier on labeled data (teacher). Self-training 1 2Self-training 3 4n What is Noisy Student? Self-training with Noisy Student. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial 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.