As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. Please We used the version from [47], which filtered the validation set of ImageNet. Self-training with Noisy Student improves ImageNet classification The performance consistently drops with noise function removed. 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. Edit social preview. Self-training with Noisy Student improves ImageNet classification. FixMatch-LS: Semi-supervised skin lesion classification with label Are labels required for improving adversarial robustness? In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards w Summary of key results compared to previous state-of-the-art models. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. We iterate this process by On . 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. 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. 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. In particular, we first perform normal training with a smaller resolution for 350 epochs. It can be seen that masks are useful in improving classification performance. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 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. [68, 24, 55, 22]. In terms of methodology, Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. 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 . Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. CLIP: Connecting text and images - OpenAI We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 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. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. unlabeled images. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). Learn more. SelfSelf-training with Noisy Student improves ImageNet classification sign in 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. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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. You signed in with another tab or window. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. 27.8 to 16.1. GitHub - google-research/noisystudent: Code for Noisy Student Training Their purpose is different from ours: to adapt a teacher model on one domain to another. Astrophysical Observatory. 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. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. CLIP (Contrastive Language-Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.The idea of zero-data learning dates back over a decade [^reference-8] but until recently was mostly studied in computer vision as a way of generalizing to unseen object categories. These CVPR 2020 papers are the Open Access versions, provided by the. Train a classifier on labeled data (teacher). Self-training with Noisy Student improves ImageNet classification To achieve strong results on ImageNet, the student model also needs to be large, typically larger than common vision models, so that it can leverage a large number of unlabeled images. Self-Training with Noisy Student Improves ImageNet Classification If nothing happens, download GitHub Desktop and try again. ; 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. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. . 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. on ImageNet ReaL As can be seen from Table 8, the performance stays similar when we reduce the data to 116 of the total data, which amounts to 8.1M images after duplicating. A tag already exists with the provided branch name. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. sign in On, International journal of molecular sciences. Self-training with Noisy Student - Medium Our work is based on self-training (e.g.,[59, 79, 56]). We use stochastic depth[29], dropout[63] and RandAugment[14]. Specifically, we train the student model for 350 epochs for models larger than EfficientNet-B4, including EfficientNet-L0, L1 and L2 and train the student model for 700 epochs for smaller models. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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 duplicate images in classes where there are not enough 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. 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. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. We iterate this process by putting back the student as the teacher. Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. 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. However, manually annotating organs from CT scans is time . 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. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. Noisy Students performance improves with more unlabeled data. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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 ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. ImageNet-A top-1 accuracy from 16.6 Self-training with Noisy Student improves ImageNet classification (using extra training data). On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Especially unlabeled images are plentiful and can be collected with ease. Self-Training : Noisy Student : First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Computer Science - Computer Vision and Pattern Recognition. Self-Training With Noisy Student Improves ImageNet Classification. This is probably because it is harder to overfit the large unlabeled dataset. Le. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. Self-Training With Noisy Student Improves ImageNet Classification unlabeled images , . 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. 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. Do imagenet classifiers generalize to imagenet? The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. If nothing happens, download Xcode and try again. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Noisy Student (EfficientNet) - huggingface.co This material is presented to ensure timely dissemination of scholarly and technical work. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The width. Code for Noisy Student Training. Are you sure you want to create this branch? 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. , have shown that computer vision models lack robustness. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. 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. Self-training with Noisy Student improves ImageNet classification 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. We then use the teacher model to generate pseudo labels on unlabeled images. Papers With Code is a free resource with all data licensed under. augmentation, dropout, stochastic depth to the student so that the noised Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. 3429-3440. . However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. 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. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the 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. 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. putting back the student as the teacher. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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]. Train a larger classifier on the combined set, adding noise (noisy student). Self-Training for Natural Language Understanding! 10687-10698 Abstract We iterate this process by putting back the student as the teacher. 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. For each class, we select at most 130K images that have the highest confidence. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Use Git or checkout with SVN using the web URL. We find that Noisy Student is better with an additional trick: data balancing. - : self-training_with_noisy_student_improves_imagenet_classification 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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. In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . Please refer to [24] for details about mFR and AlexNets flip probability. Code is available at https://github.com/google-research/noisystudent. We improved it by adding noise to the student to learn beyond the teachers knowledge. We do not tune these hyperparameters extensively since our method is highly robust to them. Finally, we iterate the algorithm a few times by treating the student as a teacher to generate new pseudo labels and train a new student. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Hence we use soft pseudo labels for our experiments unless otherwise specified. We use EfficientNet-B0 as both the teacher model and the student model and compare using Noisy Student with soft pseudo labels and hard pseudo labels. 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. Use Git or checkout with SVN using the web URL. 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. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. But training robust supervised learning models is requires this step. The results also confirm that vision models can benefit from Noisy Student even without iterative training. Work fast with our official CLI. Models are available at this https URL. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled 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. (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. The most interesting image is shown on the right of the first row. task. Here we study if it is possible to improve performance on small models by using a larger teacher model, since small models are useful when there are constraints for model size and latency in real-world applications. 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. In the following, we will first describe experiment details to achieve our results. First, we run an EfficientNet-B0 trained on ImageNet[69]. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Train a larger classifier on the combined set, adding noise (noisy student). Please The baseline model achieves an accuracy of 83.2. Are you sure you want to create this branch? The comparison is shown in Table 9. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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 also study the effects of using different amounts of unlabeled data. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. 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. These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. tsai - Noisy student Noisy Student Explained | Papers With Code 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. A semi-supervised segmentation network based on noisy student learning Notice, Smithsonian Terms of Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Self-training with Noisy Student improves ImageNet classification. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. We iterate this process by putting back the student as the teacher. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. possible. 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 Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Noisy Student can still improve the accuracy to 1.6%. Self-training with Noisy Student improves ImageNet classification
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