An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. Train a larger classifier on the combined set, adding noise (noisy student). Hence we use soft pseudo labels for our experiments unless otherwise specified. We improved it by adding noise to the student to learn beyond the teachers knowledge. Why Self-training with Noisy Students beats SOTA Image classification 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. 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. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. and surprising gains on robustness and adversarial benchmarks. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 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. IEEE Trans. Do imagenet classifiers generalize to imagenet? In other words, small changes in the input image can cause large changes to the predictions. 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. The main difference between Data Distillation and our method is that we use the noise to weaken the student, which is the opposite of their approach of strengthening the teacher by ensembling. 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. Self-training with Noisy Student improves ImageNet classification They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. We apply dropout to the final classification layer with a dropout rate of 0.5. 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. During this process, we kept increasing the size of the student model to improve the performance. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. In particular, we first perform normal training with a smaller resolution for 350 epochs. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin We start with the 130M unlabeled images and gradually reduce the number of images. to noise the student. 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. The abundance of data on the internet is vast. 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. on ImageNet ReaL. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. 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. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . [^reference-9] [^reference-10] A critical insight was to . Self-mentoring: : A new deep learning pipeline to train a self Self-Training Noisy Student " " Self-Training . PDF Self-Training with Noisy Student Improves ImageNet Classification Code is available at https://github.com/google-research/noisystudent. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. et al. Self-Training with Noisy Student Improves ImageNet Classification 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. labels, the teacher is not noised so that the pseudo labels are as good as Use Git or checkout with SVN using the web URL. 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. Self-training with Noisy Student improves ImageNet classification. Learn more. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. 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. 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. 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. 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. 2023.3.1_2 - For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. [68, 24, 55, 22]. If nothing happens, download GitHub Desktop and try again. If nothing happens, download GitHub Desktop and try again. Papers With Code is a free resource with all data licensed under. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. (or is it just me), Smithsonian Privacy We used the version from [47], which filtered the validation set of ImageNet. https://arxiv.org/abs/1911.04252. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Since a teacher models confidence on an image can be a good indicator of whether it is an out-of-domain image, we consider the high-confidence images as in-domain images and the low-confidence images as out-of-domain images. Iterative training is not used here for simplicity. 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. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. self-mentoring outperforms data augmentation and self training. 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. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model A tag already exists with the provided branch name. 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. Self-training with Noisy Student improves ImageNet classification The width. ImageNet-A top-1 accuracy from 16.6 We also list EfficientNet-B7 as a reference. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. Self-training with Noisy Student. sign in The abundance of data on the internet is vast. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. We use a resolution of 800x800 in this experiment. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. GitHub - google-research/noisystudent: Code for Noisy Student Training The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative The main difference between our method and knowledge distillation is that knowledge distillation does not consider unlabeled data and does not aim to improve the student model. 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. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Noisy Students performance improves with more unlabeled data. 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. Self-Training for Natural Language Understanding! We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. 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. 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. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 3429-3440. . EfficientNet with Noisy Student produces correct top-1 predictions (shown in. For RandAugment, we apply two random operations with the magnitude set to 27. 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 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. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. 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. You signed in with another tab or window. Are labels required for improving adversarial robustness? 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. Self-Training With Noisy Student Improves ImageNet Classification. Test images on ImageNet-P underwent different scales of perturbations. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. over the JFT dataset to predict a label for each image. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. The performance consistently drops with noise function removed. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. In terms of methodology, Their noise model is video specific and not relevant for image classification. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. . We then select images that have confidence of the label higher than 0.3. Zoph et al. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. In contrast, the predictions of the model with Noisy Student remain quite stable. But training robust supervised learning models is requires this step. Our work is based on self-training (e.g.,[59, 79, 56]). During the generation of the pseudo 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 algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. 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. 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 . arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. 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. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. Noisy Student can still improve the accuracy to 1.6%. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Models are available at this https URL. Copyright and all rights therein are retained by authors or by other copyright holders. 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]. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Here we study how to effectively use out-of-domain data. 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]. Please refer to [24] for details about mCE and AlexNets error rate. 27.8 to 16.1. In the following, we will first describe experiment details to achieve our results. 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. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. By clicking accept or continuing to use the site, you agree to the terms outlined in our. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. However, manually annotating organs from CT scans is time . It is expensive and must be done with great care. On, International journal of molecular sciences. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. , have shown that computer vision models lack robustness. In other words, the student is forced to mimic a more powerful ensemble model. IEEE Transactions on Pattern Analysis and Machine Intelligence. We iterate this process by putting back the student as the teacher. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. 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. 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. 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 improves ImageNet classification 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, 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. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. For example, with all noise removed, the accuracy drops from 84.9% to 84.3% in the case with 130M unlabeled images and drops from 83.9% to 83.2% in the case with 1.3M unlabeled images. It can be seen that masks are useful in improving classification performance. Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. The inputs to the algorithm are both labeled and unlabeled images. A tag already exists with the provided branch name. 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. Yalniz et al. We use the same architecture for the teacher and the student and do not perform iterative training. Train a classifier on labeled data (teacher). We first report the validation set accuracy on the ImageNet 2012 ILSVRC challenge prediction task as commonly done in literature[35, 66, 23, 69] (see also [55]). Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Self-training with Noisy Student improves ImageNet classification Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Noisy Student Training seeks to improve on self-training and distillation in two ways. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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. The most interesting image is shown on the right of the first row. Med. Semi-supervised medical image classification with relation-driven self-ensembling model. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Self-training with Noisy Student improves ImageNet classification 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%. CVPR 2020 Open Access Repository Figure 1(b) shows images from ImageNet-C and the corresponding predictions. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. [57] used self-training for domain adaptation. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . 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. This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. Are you sure you want to create this branch? This model investigates a new method. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.