Use Git or checkout with SVN using the web URL. 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 . Please refer to [24] for details about mCE and AlexNets error rate. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. 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. . Astrophysical Observatory. 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]. 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. unlabeled images , . We iterate this process by putting back the student as the teacher. During the generation of the pseudo 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. https://arxiv.org/abs/1911.04252. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Self-training with noisy student improves imagenet classification. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative We use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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. 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. We also list EfficientNet-B7 as a reference. 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. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. Code for Noisy Student Training. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. 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. Self-training first uses labeled data to train a good teacher model, then use the teacher model to label unlabeled data and finally use the labeled data and unlabeled data to jointly train a student model. 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. ImageNet . Noisy Student Training is a semi-supervised learning approach. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. ImageNet-A top-1 accuracy from 16.6 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. Use Git or checkout with SVN using the web URL. 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. 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. To achieve this result, we first train an EfficientNet model on labeled It is expensive and must be done with great care. 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. 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. 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. 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. A common workaround is to use entropy minimization or ramp up the consistency loss. student is forced to learn harder from the 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 use the standard augmentation instead of RandAugment in this experiment. 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. Learn more. 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. 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. 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 . Train a classifier on labeled data (teacher). During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. 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. 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. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. This model investigates a new method. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. Their noise model is video specific and not relevant for image classification. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 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. ; 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We use stochastic depth[29], dropout[63] and RandAugment[14]. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. 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 larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. We also study the effects of using different amounts of 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. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. 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. In other words, small changes in the input image can cause large changes to the predictions. On robustness test sets, it improves ImageNet-A top . putting back the student as the teacher. 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. 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 architectures for the student and teacher models can be the same or different. We do not tune these hyperparameters extensively since our method is highly robust to them. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Imaging, 39 (11) (2020), pp. Self-Training Noisy Student " " Self-Training . Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. In the following, we will first describe experiment details to achieve our results. We used the version from [47], which filtered the validation set of ImageNet. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Here we study how to effectively use out-of-domain data. We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled images. Our main results are shown in Table1. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. 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. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. 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. Self-training with Noisy Student improves ImageNet classification Abstract. 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 labels, the teacher is not noised so that the pseudo labels are as good as on ImageNet, which is 1.0 This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Due to duplications, there are only 81M unique images among these 130M images. Train a larger classifier on the combined set, adding noise (noisy student). Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. 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. to use Codespaces. Especially unlabeled images are plentiful and can be collected with ease. For classes where we have too many images, we take the images with the highest confidence. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. 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. et al. self-mentoring outperforms data augmentation and self training. Noisy Student leads to significant improvements across all model sizes for EfficientNet. sign in This is probably because it is harder to overfit the large unlabeled dataset. Code is available at https://github.com/google-research/noisystudent. We apply dropout to the final classification layer with a dropout rate of 0.5. As shown in Table2, Noisy Student with EfficientNet-L2 achieves 87.4% top-1 accuracy which is significantly better than the best previously reported accuracy on EfficientNet of 85.0%. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Add a 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. 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. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. 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. Infer labels on a much larger unlabeled dataset. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. . The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. We start with the 130M unlabeled images and gradually reduce the number of images. IEEE Trans. But during the learning of the student, we inject noise such as data 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. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. Train a larger classifier on the combined set, adding noise (noisy student). 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. We improved it by adding noise to the student to learn beyond the teachers knowledge. 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. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. 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. to noise the student. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. We iterate this process by putting back the student as the teacher. 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. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 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 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]. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Infer labels on a much larger unlabeled dataset. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. 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. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. on ImageNet ReaL. We sample 1.3M images in confidence intervals. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We iterate this process by putting back the student as the teacher. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. Zoph et al. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Please Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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. Noisy StudentImageNetEfficientNet-L2state-of-the-art. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
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