Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. 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. For RandAugment, we apply two random operations with the magnitude set to 27. Summarization_self-training_with_noisy_student_improves_imagenet But during the learning of the student, we inject noise such as data 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. 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. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. This 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. Le. 2023.3.1_2 - We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. It 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. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Iterative training is not used here for simplicity. Code for Noisy Student Training. First, a teacher model is trained in a supervised fashion. Train a classifier on labeled data (teacher). Self-Training : Noisy Student : ImageNet-A top-1 accuracy from 16.6 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. On, International journal of molecular sciences. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. 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. We use the labeled images to train a teacher model using the standard cross entropy loss. Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality The most interesting image is shown on the right of the first row. 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. Notice, Smithsonian Terms of If nothing happens, download GitHub Desktop and try again. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. 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]. 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. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. There was a problem preparing your codespace, please try again. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. 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. Agreement NNX16AC86A, Is ADS down? The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. The main use case of knowledge distillation is model compression by making the student model smaller. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. A tag already exists with the provided branch name. We then select images that have confidence of the label higher than 0.3. Learn more. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). We then perform data filtering and balancing on this corpus. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical A number of studies, e.g. In this section, we study the importance of noise and the effect of several noise methods used in our model. This is probably because it is harder to overfit the large unlabeled dataset. 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. 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. 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. unlabeled images. We then use the teacher model to generate pseudo labels on unlabeled images. Papers With Code is a free resource with all data licensed under. 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. 10687-10698 Abstract 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 with Noisy Student Improves ImageNet Classification To achieve strong results on ImageNet, the student model also needs to be large, typically larger than common vision models, so that it can leverage a large number of unlabeled images. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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.