These CVPR 2020 papers are the Open Access versions, provided by the. If nothing happens, download Xcode and try again. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. The performance consistently drops with noise function removed. This result is also a new state-of-the-art and 1% better than the previous best method that used an order of magnitude more weakly labeled data [ 44, 71]. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10687-10698, (2020 . 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. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. 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. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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. If nothing happens, download Xcode and try again. For classes where we have too many images, we take the images with the highest confidence. ImageNet . Use Git or checkout with SVN using the web URL. 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. 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. 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. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. on ImageNet, which is 1.0 Different kinds of noise, however, may have different effects. We used the version from [47], which filtered the validation set of ImageNet. Self-training with Noisy Student improves ImageNet classification. Use, Smithsonian You signed in with another tab or window. IEEE Transactions on Pattern Analysis and Machine Intelligence. on ImageNet ReaL. 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 hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. Self-Training Noisy Student " " Self-Training . 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. 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. Papers With Code is a free resource with all data licensed under. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . 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 best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. 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. 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. Add a 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. [^reference-9] [^reference-10] A critical insight was to . Abdominal organ segmentation is very important for clinical applications. IEEE Trans. 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 then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. This material is presented to ensure timely dissemination of scholarly and technical work. 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). However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. We use EfficientNet-B4 as both the teacher and the student. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. For example, with all noise removed, the accuracy drops from 84.9% to 84.3% in the case with 130M unlabeled images and drops from 83.9% to 83.2% in the case with 1.3M unlabeled images. Similar to[71], we fix the shallow layers during finetuning. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. and surprising gains on robustness and adversarial benchmarks. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Models are available at this https URL. Chowdhury et al. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative On . We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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]. 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. 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. Especially unlabeled images are plentiful and can be collected with ease. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. In particular, we first perform normal training with a smaller resolution for 350 epochs. 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. We also study the effects of using different amounts of unlabeled data. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. On robustness test sets, it improves ImageNet-A top . Self-training 1 2Self-training 3 4n What is Noisy Student? In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. Different types of. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Self-training with Noisy Student improves ImageNet classification Original paper: https://arxiv.org/pdf/1911.04252.pdf Authors: Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le HOYA012 Introduction EfficientNet ImageNet SOTA EfficientNet A tag already exists with the provided branch name. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. 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. 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 Do better imagenet models transfer better? Code is available at https://github.com/google-research/noisystudent. 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. For more information about the large architectures, please refer to Table7 in Appendix A.1. over the JFT dataset to predict a label for each image. sign in The inputs to the algorithm are both labeled and unlabeled images. 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. 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 . Please 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. 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 top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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. Self-training 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. 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 iterate this process by putting back the student as the teacher. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Work fast with our official CLI. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: For ImageNet checkpoints trained by Noisy Student Training, please refer to the EfficientNet github. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. 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 start with the 130M unlabeled images and gradually reduce the number of images. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. To intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. We do not tune these hyperparameters extensively since our method is highly robust to them. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. 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. 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. Proceedings of the eleventh annual conference on Computational learning theory, Proceedings of the IEEE conference on computer vision and pattern recognition, Empirical Methods in Natural Language Processing (EMNLP), Imagenet classification with deep convolutional neural networks, Domain adaptive transfer learning with specialist models, Thirty-Second AAAI Conference on Artificial Intelligence, Regularized evolution for image classifier architecture search, Inception-v4, inception-resnet and the impact of residual connections on learning.
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