self training with noisy student improves imagenet classification

sign in It is expensive and must be done with great care. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. The width. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. sign in 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. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. 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. 10687-10698 Abstract We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . on ImageNet ReaL. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. We then use the teacher model to generate pseudo labels on unlabeled images. These CVPR 2020 papers are the Open Access versions, provided by the. We use the labeled images to train a teacher model using the standard cross entropy loss. 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. 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. 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. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. 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. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. Work fast with our official CLI. Work fast with our official CLI. Our work is based on self-training (e.g.,[59, 79, 56]). This model investigates a new method. Are labels required for improving adversarial robustness? [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Are you sure you want to create this branch? The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. 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. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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 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]. Self-training with Noisy Student improves ImageNet classification. We improved it by adding noise to the student to learn beyond the teachers knowledge. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Copyright and all rights therein are retained by authors or by other copyright holders. 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. 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. 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. 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. Self-training 1 2Self-training 3 4n What is Noisy Student? 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. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. We iterate this process by putting back the student as the teacher. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. In particular, we first perform normal training with a smaller resolution for 350 epochs. Hence we use soft pseudo labels for our experiments unless otherwise specified. We do not tune these hyperparameters extensively since our method is highly robust to them. Finally, in the above, we say that the pseudo labels can be soft or hard. 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. , have shown that computer vision models lack robustness. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Learn more. on ImageNet ReaL The algorithm is basically self-training, a method in semi-supervised learning (. [^reference-9] [^reference-10] A critical insight was to . 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. Use Git or checkout with SVN using the web URL. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. w Summary of key results compared to previous state-of-the-art models. 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. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 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. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. A tag already exists with the provided branch name. First, a teacher model is trained in a supervised fashion. 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. 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. 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. If nothing happens, download Xcode and try again. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. . 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 top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. combination of labeled and pseudo labeled 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. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. 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). Code for Noisy Student Training. During the generation of the pseudo 10687-10698). 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. 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. 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). We then perform data filtering and balancing on this corpus. 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. Noisy Student Training is a semi-supervised learning approach. 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. 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. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. 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. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled 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. 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. This invariance constraint reduces the degrees of freedom in the model. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. We iterate this process by putting back the student as the teacher. 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. 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. 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]. For unlabeled images, we set the batch size to be three times the batch size of labeled images for large models, including EfficientNet-B7, L0, L1 and L2. (using extra training data). 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. Le. 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. 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. 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. 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. 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. This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. on ImageNet, which is 1.0 Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. As shown in Table3,4 and5, when compared with the previous state-of-the-art model ResNeXt-101 WSL[44, 48] trained on 3.5B weakly labeled images, Noisy Student yields substantial gains on robustness datasets. The main use case of knowledge distillation is model compression by making the student model smaller. Train a classifier on labeled data (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.

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