Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. 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. Noise Self-training with Noisy Student 1. Le. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. On . The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Different types of. Chowdhury et al. Self-training is a form of semi-supervised learning [10] which attempts to leverage unlabeled data to improve classification performance in the limited data regime. The architectures for the student and teacher models can be the same or different. 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. 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. 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. Please Flip probability is the probability that the model changes top-1 prediction for different perturbations. In other words, small changes in the input image can cause large changes to the predictions. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. student is forced to learn harder from the pseudo labels. A common workaround is to use entropy minimization or ramp up the consistency loss. We used the version from [47], which filtered the validation set of ImageNet. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Self-training with Noisy Student improves ImageNet classification. It can be seen that masks are useful in improving classification performance. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. For RandAugment, we apply two random operations with the magnitude set to 27. Then, that teacher is used to label the unlabeled data. Please refer to [24] for details about mFR and AlexNets flip probability. 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. You signed in with another tab or window. If nothing happens, download GitHub Desktop and try again. 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. We then train a larger EfficientNet as a student model on the Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. We will then show our results on ImageNet and compare them with state-of-the-art models. These CVPR 2020 papers are the Open Access versions, provided by the. Med. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. augmentation, dropout, stochastic depth to the student so that the noised 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. Do better imagenet models transfer better? Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. to use Codespaces. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. 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. 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 shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. 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. 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]. The width. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. (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. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Work fast with our official CLI. Soft pseudo labels lead to better performance for low confidence data. combination of labeled and pseudo labeled images. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. During this process, we kept increasing the size of the student model to improve the performance. Our study shows that using unlabeled data improves accuracy and general robustness. Different kinds of noise, however, may have different effects. 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 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.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 inputs to the algorithm are both labeled and unlabeled images. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. For more information about the large architectures, please refer to Table7 in Appendix A.1. Here we study how to effectively use out-of-domain data. Abdominal organ segmentation is very important for clinical applications. 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. 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. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. Self-training with Noisy Student improves ImageNet classification. 27.8 to 16.1. Our procedure went as follows. team using this approach not only surpasses the top-1 ImageNet accuracy of SOTA models by 1%, it also shows that the robustness of a model also improves.