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. 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. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. 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 then select images that have confidence of the label higher than 0.3. We use the labeled images to train a teacher model using the standard cross entropy loss. Models are available at this https URL. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. Self-training with Noisy Student improves ImageNet classification. Finally, in the above, we say that the pseudo labels can be soft or hard. Our main results are shown in Table1. 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. 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. We also study the effects of using different amounts of unlabeled data. We duplicate images in classes where there are not enough images. 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 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. Due to duplications, there are only 81M unique images among these 130M images. Do imagenet classifiers generalize to imagenet? 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. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Parthasarathi et al. unlabeled images , . They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. Code is available at https://github.com/google-research/noisystudent. 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. 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]. 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. In other words, the student is forced to mimic a more powerful ensemble model. Their noise model is video specific and not relevant for image classification. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Train a classifier on labeled data (teacher). In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Please refer to [24] for details about mFR and AlexNets flip probability. Image Classification To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. 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. See Self-training There was a problem preparing your codespace, please try again. 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 . Agreement NNX16AC86A, Is ADS down? We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. It is found that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. IEEE Trans. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. The paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task is revisited, and a simple recipe that is called Big Transfer (BiT) is created, which achieves strong performance on over 20 datasets. 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. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. unlabeled images. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. 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. Train a larger classifier on the combined set, adding noise (noisy student). Please refer to [24] for details about mCE and AlexNets error rate. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Self-training with Noisy Student improves ImageNet classification. However an important requirement for Noisy Student to work well is that the student model needs to be sufficiently large to fit more data (labeled and pseudo labeled). Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. Similar to[71], we fix the shallow layers during finetuning. 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. 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. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. The width. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. 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 Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. As shown in Table3,4 and5, when compared with the previous state-of-the-art model ResNeXt-101 WSL[44, 48] trained on 3.5B weakly labeled images, Noisy Student yields substantial gains on robustness datasets. If nothing happens, download GitHub Desktop and try again. 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. On robustness test sets, it improves Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. 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.