We then train a larger EfficientNet as a student model on the We iterate this process by putting back the student as the teacher. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The model with Noisy Student can successfully predict the correct labels of these highly difficult 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. 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. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. On robustness test sets, it improves We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. tsai - Noisy student Due to duplications, there are only 81M unique images among these 130M images. Please refer to [24] for details about mFR and AlexNets flip probability. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and We sample 1.3M images in confidence intervals. 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. 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). With Noisy Student, the model correctly predicts dragonfly for the image. student is forced to learn harder from the pseudo labels. Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. We iterate this process by putting back the student as the teacher. Our procedure went as follows. The performance drops when we further reduce it. Noisy Student Explained | Papers With Code EfficientNet with Noisy Student produces correct top-1 predictions (shown in. Do imagenet classifiers generalize to imagenet? 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 . This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. Are you sure you want to create this branch? 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Next, a larger student model is trained on the combination of all data and achieves better performance than the teacher by itself.OUTLINE:0:00 - Intro \u0026 Overview1:05 - Semi-Supervised \u0026 Transfer Learning5:45 - Self-Training \u0026 Knowledge Distillation10:00 - Noisy Student Algorithm Overview20:20 - Noise Methods22:30 - Dataset Balancing25:20 - Results30:15 - Perturbation Robustness34:35 - Ablation Studies39:30 - Conclusion \u0026 CommentsPaper: https://arxiv.org/abs/1911.04252Code: https://github.com/google-research/noisystudentModels: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnetAbstract:We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. [^reference-9] [^reference-10] A critical insight was to . Self-training with Noisy Student improves ImageNet classification 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. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. 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. (or is it just me), Smithsonian Privacy over the JFT dataset to predict a label for each image. Then, that teacher is used to label the unlabeled data. Our study shows that using unlabeled data improves accuracy and general robustness. Work fast with our official CLI. Hence we use soft pseudo labels for our experiments unless otherwise specified. 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. Noisy Student leads to significant improvements across all model sizes for EfficientNet. The learning rate starts at 0.128 for labeled batch size 2048 and decays by 0.97 every 2.4 epochs if trained for 350 epochs or every 4.8 epochs if trained for 700 epochs. Copyright and all rights therein are retained by authors or by other copyright holders. 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. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Self-training 1 2Self-training 3 4n What is Noisy Student? Self-training with Noisy Student improves ImageNet classification For each class, we select at most 130K images that have the highest confidence. 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. Self-training with Noisy Student improves ImageNet classification Abstract. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Self-Training with Noisy Student Improves ImageNet Classification Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. For instance, on ImageNet-A, Noisy Student achieves 74.2% top-1 accuracy which is approximately 57% more accurate than the previous state-of-the-art model. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 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. 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. 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. Image Classification We also study the effects of using different amounts of unlabeled data. 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 then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. . We use EfficientNet-B4 as both the teacher and the student. We used the version from [47], which filtered the validation set of ImageNet. unlabeled 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. . 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. On robustness test sets, it improves ImageNet-A top . The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. 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. Different kinds of noise, however, may have different effects. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. Train a classifier on labeled data (teacher). 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Since a teacher models confidence on an image can be a good indicator of whether it is an out-of-domain image, we consider the high-confidence images as in-domain images and the low-confidence images as out-of-domain images. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. 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]. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. putting back the student as the teacher. We use stochastic depth[29], dropout[63] and RandAugment[14]. We iterate this process by putting back the student as the teacher. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. On . Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Self-Training With Noisy Student Improves ImageNet Classification 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. It implements SemiSupervised Learning with Noise to create an Image Classification. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Summarization_self-training_with_noisy_student_improves_imagenet_classification. The most interesting image is shown on the right of the first row. The main use case of knowledge distillation is model compression by making the student model smaller. - : self-training_with_noisy_student_improves_imagenet_classification We iterate this process by Learn more. Please In the following, we will first describe experiment details to achieve our results. combination of labeled and pseudo labeled images. Self-Training for Natural Language Understanding! 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. Here we study how to effectively use out-of-domain data. 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. 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. 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. Self-Training With Noisy Student Improves ImageNet Classification Their noise model is video specific and not relevant for image classification. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Med. But during the learning of the student, we inject noise such as data 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. Self-training with Noisy Student improves ImageNet classification If nothing happens, download GitHub Desktop and try again. 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 top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. We will then show our results on ImageNet and compare them with 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. In particular, we first perform normal training with a smaller resolution for 350 epochs. [57] used self-training for domain adaptation. We duplicate images in classes where there are not enough images. 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. 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. 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. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. It is expensive and must be done with great care. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 We start with the 130M unlabeled images and gradually reduce the number of images. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. Notice, Smithsonian Terms of 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. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. International Conference on Machine Learning, Learning extraction patterns for subjective expressions, Proceedings of the 2003 conference on Empirical methods in natural language processing, A. Roy Chowdhury, P. Chakrabarty, A. Singh, S. Jin, H. Jiang, L. Cao, and E. G. Learned-Miller, Automatic adaptation of object detectors to new domains using self-training, T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, Probability of error of some adaptive pattern-recognition machines, W. Shi, Y. Gong, C. Ding, Z. MaXiaoyu Tao, and N. Zheng, Transductive semi-supervised deep learning using min-max features, C. Simon-Gabriel, Y. Ollivier, L. Bottou, B. Schlkopf, and D. Lopez-Paz, First-order adversarial vulnerability of neural networks and input dimension, Very deep convolutional networks for large-scale image recognition, N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. We find that Noisy Student is better with an additional trick: data balancing. Train a larger classifier on the combined set, adding noise (noisy student). 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. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. PDF Self-Training with Noisy Student Improves ImageNet Classification 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. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. 27.8 to 16.1. If nothing happens, download GitHub Desktop and try again. to use Codespaces. There was a problem preparing your codespace, please try again. Add a possible. Our experiments show that an important element for this simple method to work well at scale is that the student model should be noised during its training while the teacher should not be noised during the generation of pseudo labels. 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. Edit social preview. Self-training with Noisy Student improves ImageNet classification When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single model. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. 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. The ONCE (One millioN sCenEs) dataset for 3D object detection in the autonomous driving scenario is introduced and a benchmark is provided in which a variety of self-supervised and semi- supervised methods on the ONCE dataset are evaluated. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. 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. But training robust supervised learning models is requires this step. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Although they have produced promising results, in our preliminary experiments, consistency regularization works less well on ImageNet because consistency regularization in the early phase of ImageNet training regularizes the model towards high entropy predictions, and prevents it from achieving good accuracy. Further, Noisy Student outperforms the state-of-the-art accuracy of 86.4% by FixRes ResNeXt-101 WSL[44, 71] that requires 3.5 Billion Instagram images labeled with tags. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. 3.5B weakly labeled Instagram images. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. all 12, Image Classification SelfSelf-training with Noisy Student improves ImageNet classification We do not tune these hyperparameters extensively since our method is highly robust to them. A tag already exists with the provided branch name. Yalniz et al. Especially unlabeled images are plentiful and can be collected with ease. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. In other words, the student is forced to mimic a more powerful ensemble model. 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. 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. There was a problem preparing your codespace, please try again. Are labels required for improving adversarial robustness? w Summary of key results compared to previous state-of-the-art models. and surprising gains on robustness and adversarial benchmarks. 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 . In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. ; 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. A number of studies, e.g. Self-training with Noisy Student improves ImageNet classification The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. Noisy Student Training seeks to improve on self-training and distillation in two ways. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. 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. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. 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.

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