self training with noisy student improves imagenet classification

A tag already exists with the provided branch name. 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. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Do imagenet classifiers generalize to imagenet? We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. The width. Copyright and all rights therein are retained by authors or by other copyright holders. Infer labels on a much larger unlabeled dataset. We use the labeled images to train a teacher model using the standard cross entropy loss. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 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. 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. A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet. 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. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. Use, Smithsonian 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. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. If nothing happens, download Xcode and try again. Chowdhury et al. The baseline model achieves an accuracy of 83.2. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 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. We duplicate images in classes where there are not enough images. On robustness test sets, it improves ImageNet-A top . Self-Training With Noisy Student Improves ImageNet Classification. 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. The abundance of data on the internet is vast. We iterate this process by task. 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. We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. [57] used self-training for domain adaptation. We improved it by adding noise to the student to learn beyond the teachers knowledge. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. , have shown that computer vision models lack robustness. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. The main use case of knowledge distillation is model compression by making the student model smaller. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Med. 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. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. We apply dropout to the final classification layer with a dropout rate of 0.5. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Test images on ImageNet-P underwent different scales of perturbations. 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. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. A common workaround is to use entropy minimization or ramp up the consistency loss. A number of studies, e.g. However, manually annotating organs from CT scans is time . On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Agreement NNX16AC86A, Is ADS down? In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. In this section, we study the importance of noise and the effect of several noise methods used in our model. labels, the teacher is not noised so that the pseudo labels are as good as About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. . For RandAugment, we apply two random operations with the magnitude set to 27. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. 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. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. 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. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. . Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. (using extra training data). Their purpose is different from ours: to adapt a teacher model on one domain to another. 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 use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. 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. In other words, the student is forced to mimic a more powerful ensemble model. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. . 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. 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. 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 The most interesting image is shown on the right of the first row. Use Git or checkout with SVN using the web URL. 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. 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. In typical self-training with the teacher-student framework, noise injection to the student is not used by default, or the role of noise is not fully understood or justified. https://arxiv.org/abs/1911.04252. Astrophysical Observatory. 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 (B7) means to use EfficientNet-B7 for both the student and the teacher. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Le. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 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. 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. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. IEEE Trans. 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. At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. [^reference-9] [^reference-10] A critical insight was to . . 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. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. 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 proposes a novel architectural unit, which is term the Squeeze-and-Excitation (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and shows that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. 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. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. We also list EfficientNet-B7 as a reference. 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. 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. We then use the teacher model to generate pseudo labels on unlabeled images. 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. To achieve this result, we first train an EfficientNet model on labeled Semi-supervised medical image classification with relation-driven self-ensembling model. IEEE Transactions on Pattern Analysis and Machine Intelligence. Their noise model is video specific and not relevant for image classification. 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. Please Image Classification The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative 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]. Add a 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]. If nothing happens, download GitHub Desktop and try again. 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. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. 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. 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. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. 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. Soft pseudo labels lead to better performance for low confidence data. If nothing happens, download GitHub Desktop and try again. self-mentoring outperforms data augmentation and self training. 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. 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. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . to noise the student. 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 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. Work fast with our official CLI. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. 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. By clicking accept or continuing to use the site, you agree to the terms outlined in our. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. 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 . Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. 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. Abdominal organ segmentation is very important for clinical applications. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. 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. 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. Noisy Student Training is a semi-supervised learning approach. Please The abundance of data on the internet is vast. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Train a classifier on labeled data (teacher). ImageNet . 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. We iterate this process by putting back the student as the teacher. Noisy StudentImageNetEfficientNet-L2state-of-the-art. augmentation, dropout, stochastic depth to the student so that the noised 3429-3440. . The comparison is shown in Table 9. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . 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. 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. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. 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. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. We do not tune these hyperparameters extensively since our method is highly robust to them. For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment. 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. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and In contrast, the predictions of the model with Noisy Student remain quite stable. Iterative training is not used here for simplicity. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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. You signed in with another tab or window. For each class, we select at most 130K images that have the highest confidence. 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. It is expensive and must be done with great care. 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. 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 Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. - : self-training_with_noisy_student_improves_imagenet_classification We will then show our results on ImageNet and compare them with state-of-the-art models. 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. [68, 24, 55, 22]. We iterate this process by putting back the student as the teacher. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . ; 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.

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