Self-training with noisy student improves imagenet classification. . unlabeled images. 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. We then perform data filtering and balancing on this corpus. 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). In other words, the student is forced to mimic a more powerful ensemble model. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Train a larger classifier on the combined set, adding noise (noisy student). Hence the total number of images that we use for training a student model is 130M (with some duplicated images). 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]. Self-mentoring: : A new deep learning pipeline to train a self 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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. 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 iterate this process by putting back the student as the teacher. Self-Training : Noisy Student : The performance consistently drops with noise function removed. Models are available at this https URL. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. The algorithm is basically self-training, a method in semi-supervised learning (. 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 . 2023.3.1_2 - 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. sign in Are labels required for improving adversarial robustness? 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. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images To achieve this result, we first train an EfficientNet model on labeled Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. We iterate this process by putting back the student as the teacher. First, we run an EfficientNet-B0 trained on ImageNet[69]. If nothing happens, download GitHub Desktop and try again. IEEE Transactions on Pattern Analysis and Machine Intelligence. 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. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. Are you sure you want to create this branch? Noisy Student can still improve the accuracy to 1.6%. This model investigates a new method. The abundance of data on the internet is vast. Especially unlabeled images are plentiful and can be collected with ease. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. The main use case of knowledge distillation is model compression by making the student model smaller. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Med. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. Papers With Code is a free resource with all data licensed under. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. 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 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. We use the same architecture for the teacher and the student and do not perform iterative training. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Ranked #14 on Similar to[71], we fix the shallow layers during finetuning. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. 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 evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 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. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. Finally, in the above, we say that the pseudo labels can be soft or hard. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The most interesting image is shown on the right of the first row. ; 2006)[book reviews], Semi-supervised deep learning with memory, Proceedings of the European Conference on Computer Vision (ECCV), Xception: deep learning with depthwise separable convolutions, K. Clark, M. Luong, C. D. Manning, and Q. V. Le, Semi-supervised sequence modeling with cross-view training, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, AutoAugment: learning augmentation strategies from data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, RandAugment: practical data augmentation with no separate search, Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, Good semi-supervised learning that requires a bad gan, T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, A. Galloway, A. Golubeva, T. Tanay, M. Moussa, and G. W. Taylor, R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow, I. J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and harnessing adversarial examples, Semi-supervised learning by entropy minimization, Advances in neural information processing systems, K. Gu, B. Yang, J. Ngiam, Q. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. We use the standard augmentation instead of RandAugment in this experiment. In other words, small changes in the input image can cause large changes to the predictions. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. 10687-10698 Abstract Iterative training is not used here for simplicity. Self-Training With Noisy Student Improves ImageNet Classification During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). et al. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. 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. It can be seen that masks are useful in improving classification performance. 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. FixMatch-LS: Semi-supervised skin lesion classification with label 3.5B weakly labeled Instagram 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. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Self-training The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. Self-Training With Noisy Student Improves ImageNet Classification. This material is presented to ensure timely dissemination of scholarly and technical work. The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. Self-training with Noisy Student improves ImageNet classification Abstract. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Do better imagenet models transfer better? Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Self-training with Noisy Student improves ImageNet classification Distillation Survey : Noisy Student | 9to5Tutorial 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. Learn more. [^reference-9] [^reference-10] A critical insight was to . Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. 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 classification About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . 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.
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