self training with noisy student improves imagenet classification

We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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. Infer labels on a much larger unlabeled dataset. Imaging, 39 (11) (2020), pp. student is forced to learn harder from the 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. on ImageNet ReaL Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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. 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. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. (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. 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 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. 3429-3440. . 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). [^reference-9] [^reference-10] A critical insight was to . The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. We do not tune these hyperparameters extensively since our method is highly robust to them. Due to duplications, there are only 81M unique images among these 130M images. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Works based on pseudo label[37, 31, 60, 1] are similar to self-training, but also suffers the same problem with consistency training, since it relies on a model being trained instead of a converged model with high accuracy to generate pseudo labels. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. 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 . Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. 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. 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. . On robustness test sets, it improves ImageNet-A top . 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. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. We iterate this process by putting back the student as the teacher. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. A. Alemi, Thirty-First AAAI Conference on Artificial Intelligence, C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, EfficientNet: rethinking model scaling for convolutional neural networks, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, A. Vedaldi, M. Douze, and H. Jgou, Fixing the train-test resolution discrepancy, V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), J. Weston, F. Ratle, H. Mobahi, and R. Collobert, Deep learning via semi-supervised embedding, Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le, Unsupervised data augmentation for consistency training, S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, I. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 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. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 3.5B weakly labeled Instagram images. You signed in with another tab or window. Code for Noisy Student Training. Abdominal organ segmentation is very important for clinical applications. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. On, International journal of molecular sciences. 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). The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. It 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. Le. We iterate this process by putting back the student as the teacher. The performance drops when we further reduce it. It implements SemiSupervised Learning with Noise to create an Image Classification. [68, 24, 55, 22]. to use Codespaces. 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. On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We first report the validation set accuracy on the ImageNet 2012 ILSVRC challenge prediction task as commonly done in literature[35, 66, 23, 69] (see also [55]). We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. unlabeled 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]. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative 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. 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. 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. 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. Not only our method improves standard ImageNet accuracy, it also improves classification robustness on much harder test sets by large margins: ImageNet-A[25] top-1 accuracy from 16.6% to 74.2%, ImageNet-C[24] mean corruption error (mCE) from 45.7 to 31.2 and ImageNet-P[24] mean flip rate (mFR) from 27.8 to 16.1. 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. But training robust supervised learning models is requires this step. w Summary of key results compared to previous state-of-the-art models. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and Zoph et al. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. A. Krizhevsky, I. Sutskever, and G. E. Hinton, Temporal ensembling for semi-supervised learning, Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks, Workshop on Challenges in Representation Learning, ICML, Certainty-driven consistency loss for semi-supervised learning, C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, R. G. Lopes, D. Yin, B. Poole, J. Gilmer, and E. D. Cubuk, Improving robustness without sacrificing accuracy with patch gaussian augmentation, Y. Luo, J. Zhu, M. Li, Y. Ren, and B. Zhang, Smooth neighbors on teacher graphs for semi-supervised learning, L. Maale, C. K. Snderby, S. K. Snderby, and O. Winther, A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, Towards deep learning models resistant to adversarial attacks, D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten, Exploring the limits of weakly supervised pretraining, T. Miyato, S. Maeda, S. Ishii, and M. Koyama, Virtual adversarial training: a regularization method for supervised and semi-supervised learning, IEEE transactions on pattern analysis and machine intelligence, A. Najafi, S. Maeda, M. Koyama, and T. Miyato, Robustness to adversarial perturbations in learning from incomplete data, J. Ngiam, D. Peng, V. Vasudevan, S. Kornblith, Q. V. Le, and R. Pang, Robustness properties of facebooks resnext wsl models, Adversarial dropout for supervised and semi-supervised learning, Lessons from building acoustic models with a million hours of speech, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), S. Qiao, W. Shen, Z. Zhang, B. Wang, and A. Yuille, Deep co-training for semi-supervised image recognition, I. Radosavovic, P. Dollr, R. Girshick, G. Gkioxari, and K. He, Data distillation: towards omni-supervised learning, A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, Semi-supervised learning with ladder networks, E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, Proceedings of the AAAI Conference on Artificial Intelligence, B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. Train a classifier on labeled data (teacher). Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. For classes where we have too many images, we take the images with the highest confidence. 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 call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. 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. During this process, we kept increasing the size of the student model to improve the performance. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. ImageNet-A top-1 accuracy from 16.6 We iterate this process by putting back the student as the teacher. We find that Noisy Student is better with an additional trick: data balancing. 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 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. 27.8 to 16.1. Self-Training With Noisy Student Improves ImageNet Classification. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. LeLinks:YouTube: https://www.youtube.com/c/yannickilcherTwitter: https://twitter.com/ykilcherDiscord: https://discord.gg/4H8xxDFBitChute: https://www.bitchute.com/channel/yannic-kilcherMinds: https://www.minds.com/ykilcherParler: https://parler.com/profile/YannicKilcherLinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/If you want to support me, the best thing to do is to share out the content :)If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):SubscribeStar (preferred to Patreon): https://www.subscribestar.com/yannickilcherPatreon: https://www.patreon.com/yannickilcherBitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cqEthereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9mMonero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Noisy Student Training seeks to improve on self-training and distillation in two ways. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Models are available at this https URL. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. This invariance constraint reduces the degrees of freedom in the model. But during the learning of the student, we inject noise such as data On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . 10687-10698). 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. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. 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]. Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le Description: 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. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. Noisy Student leads to significant improvements across all model sizes for EfficientNet. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. - : self-training_with_noisy_student_improves_imagenet_classification During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. The baseline model achieves an accuracy of 83.2. 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. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). 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. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. In this section, we study the importance of noise and the effect of several noise methods used in our model. Summarization_self-training_with_noisy_student_improves_imagenet_classification. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Their main goal is to find a small and fast model for deployment. 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. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. The architectures for the student and teacher models can be the same or different. 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. This material is presented to ensure timely dissemination of scholarly and technical work. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model.

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