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. 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. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. Why Self-training with Noisy Students beats SOTA Image classification 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. to use Codespaces. [57] used self-training for domain adaptation. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. Add a In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Do imagenet classifiers generalize to imagenet? Self-Training With Noisy Student Improves ImageNet Classification We use EfficientNet-B4 as both the teacher and the student. On, International journal of molecular sciences. On . Self-training with noisy student improves imagenet classification. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 There was a problem preparing your codespace, please try again. . 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 model investigates a new method. The inputs to the algorithm are both labeled and unlabeled images. 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). Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 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 present a simple self-training method that achieves 87.4 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. 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. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical 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. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. The mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. It implements SemiSupervised Learning with Noise to create an Image Classification. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. Self-Training Noisy Student " " Self-Training . Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. Noisy Student Training seeks to improve on self-training and distillation in two ways. The abundance of data on the internet is vast. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. We use a resolution of 800x800 in this experiment. First, a teacher model is trained in a supervised fashion. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. We also study the effects of using different amounts of unlabeled data. These CVPR 2020 papers are the Open Access versions, provided by the. FixMatch-LS: Semi-supervised skin lesion classification with label We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. Different kinds of noise, however, may have different effects. . Hence we use soft pseudo labels for our experiments unless otherwise specified. 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. Noisy Student (B7, L2) means to use EfficientNet-B7 as the student and use our best model with 87.4% accuracy as the teacher model. Are you sure you want to create this branch? Their noise model is video specific and not relevant for image classification. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. on ImageNet, which is 1.0 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. While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. However, in the case with 130M unlabeled images, with noise function removed, the performance is still improved to 84.3% from 84.0% when compared to the supervised baseline. 3429-3440. . 10687-10698 Abstract In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. ImageNet-A top-1 accuracy from 16.6 This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. When the student model is deliberately noised it is actually trained to be consistent to the more powerful teacher model that is not noised when it generates pseudo labels. Copyright and all rights therein are retained by authors or by other copyright holders. putting back the student as the teacher. Self-Training With Noisy Student Improves ImageNet Classification 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. Infer labels on a much larger unlabeled dataset. After using the masks generated by teacher-SN, the classification performance improved by 0.2 of AC, 1.2 of SP, and 0.7 of AUC. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. Our procedure went as follows. 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. 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. w Summary of key results compared to previous state-of-the-art models. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. ImageNet images and use it as a teacher to generate pseudo labels on 300M The most interesting image is shown on the right of the first row. . Zoph et al. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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Noisy Students performance improves with more unlabeled data. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Since we use soft pseudo labels generated from the teacher model, when the student is trained to be exactly the same as the teacher model, the cross entropy loss on unlabeled data would be zero and the training signal would vanish. 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. 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. If nothing happens, download GitHub Desktop and try again. We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled images. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. 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. In particular, we first perform normal training with a smaller resolution for 350 epochs. Here we use unlabeled images to improve the state-of-the-art ImageNet accuracy and show that the accuracy gain has an outsized impact on robustness. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. Use, Smithsonian Work fast with our official CLI. 2023.3.1_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. 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. We sample 1.3M images in confidence intervals. Although noise may appear to be limited and uninteresting, when it is applied to unlabeled data, it has a compound benefit of enforcing local smoothness in the decision function on both labeled and unlabeled data. Self-mentoring: : A new deep learning pipeline to train a self Papers With Code is a free resource with all data licensed under. Self-training with Noisy Student improves ImageNet classification Self-training with Noisy Student improves ImageNet classification. 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. 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. Please refer to [24] for details about mCE and AlexNets error rate. unlabeled images , . Please Similar to[71], we fix the shallow layers during finetuning. We use the labeled images to train a teacher model using the standard cross entropy loss. Their purpose is different from ours: to adapt a teacher model on one domain to another. Self-Training With Noisy Student Improves ImageNet Classification tsai - Noisy student Self-Training : Noisy Student : If you get a better model, you can use the model to predict pseudo-labels on the filtered data. 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. Self-Training With Noisy Student Improves ImageNet Classification We then use the teacher model to generate pseudo labels on unlabeled 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. Finally, in the above, we say that the pseudo labels can be soft or hard. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. 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. In contrast, the predictions of the model with Noisy Student remain quite stable. 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. Train a classifier on labeled data (teacher). (or is it just me), Smithsonian Privacy Computer Science - Computer Vision and Pattern Recognition. 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. 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. Please refer to [24] for details about mFR and AlexNets flip probability. 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. 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. If nothing happens, download Xcode and try again. Compared to consistency training[45, 5, 74], the self-training / teacher-student framework is better suited for ImageNet because we can train a good teacher on ImageNet using label data. 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. 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. On robustness test sets, it improves ImageNet-A top . Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. on ImageNet ReaL. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images 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. Notice, Smithsonian Terms of 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 other words, the student is forced to mimic a more powerful ensemble model. We iterate this process by Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. 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]. 3.5B weakly labeled Instagram images. 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. 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.