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. 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. Code for Noisy Student Training. This material is presented to ensure timely dissemination of scholarly and technical work. In terms of methodology, The performance drops when we further reduce it. 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). (using extra training data). On robustness test sets, it improves Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y. The most interesting image is shown on the right of the first row. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Do better imagenet models transfer better? On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . task. [68, 24, 55, 22]. . We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. ImageNet . We iterate this process by putting back the student as the teacher. Use Git or checkout with SVN using the web URL. Please refer to [24] for details about mFR and AlexNets flip probability. Code is available at https://github.com/google-research/noisystudent. Train a classifier on labeled data (teacher). Yalniz et al. 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. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from First, we run an EfficientNet-B0 trained on ImageNet[69]. (or is it just me), Smithsonian Privacy Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. This is probably because it is harder to overfit the large unlabeled dataset. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. labels, the teacher is not noised so that the pseudo labels are as good as 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. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. If nothing happens, download Xcode and try again. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. Self-Training with Noisy Student Improves ImageNet Classification Learn more. Here we study how to effectively use out-of-domain data. We do not tune these hyperparameters extensively since our method is highly robust to them. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 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. We iterate this process by putting back the student as the teacher. 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. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. The comparison is shown in Table 9. IEEE Trans. We then select images that have confidence of the label higher than 0.3. . Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. 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 width. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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. 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 find that Noisy Student is better with an additional trick: data balancing. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. ImageNet images and use it as a teacher to generate pseudo labels on 300M In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. 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. 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 . 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Noisy Student leads to significant improvements across all model sizes for EfficientNet. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. You signed in with another tab or window. Note that these adversarial robustness results are not directly comparable to prior works since we use a large input resolution of 800x800 and adversarial vulnerability can scale with the input dimension[17, 20, 19, 61]. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). Notice, Smithsonian Terms of After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 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. 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. 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. If nothing happens, download Xcode and try again. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. The algorithm is basically self-training, a method in semi-supervised learning (. We use the labeled images to train a teacher model using the standard cross entropy loss. A tag already exists with the provided branch name. 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. Our procedure went as follows. 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. Self-training with Noisy Student improves ImageNet classification. There was a problem preparing your codespace, please try again. Self-training with noisy student improves imagenet classification. For more information about the large architectures, please refer to Table7 in Appendix A.1. Self-training with Noisy Student improves ImageNet classification. 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. A number of studies, e.g. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. on ImageNet, which is 1.0 to use Codespaces. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. These significant gains in robustness in ImageNet-C and ImageNet-P are surprising because our models were not deliberately optimizing for robustness (e.g., via data augmentation). This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. We iterate this process by putting back the student as the teacher. combination of labeled and pseudo labeled images. We duplicate images in classes where there are not enough images. . We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 10687-10698 Abstract Our main results are shown in Table1. 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. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Summarization_self-training_with_noisy_student_improves_imagenet_classification. all 12, Image Classification (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. Hence we use soft pseudo labels for our experiments unless otherwise specified. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Are you sure you want to create this branch? 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. et al. 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. For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. The abundance of data on the internet is vast. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Finally, in the above, we say that the pseudo labels can be soft or hard. possible. 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 apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Self-training with Noisy Student. 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. Code for Noisy Student Training. 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. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. 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. 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%. The learning rate starts at 0.128 for labeled batch size 2048 and decays by 0.97 every 2.4 epochs if trained for 350 epochs or every 4.8 epochs if trained for 700 epochs. to noise the student. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. In other words, small changes in the input image can cause large changes to the predictions. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). Ranked #14 on Noisy Student Training seeks to improve on self-training and distillation in two ways. 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. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. 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. In this section, we study the importance of noise and the effect of several noise methods used in our model. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. But training robust supervised learning models is requires this step. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. On . We use stochastic depth[29], dropout[63] and RandAugment[14]. In contrast, the predictions of the model with Noisy Student remain quite stable. Here we show the evidence in Table 6, noise such as stochastic depth, dropout and data augmentation plays an important role in enabling the student model to perform better than the teacher. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Iterative training is not used here for simplicity. 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. Self-Training With Noisy Student Improves ImageNet Classification. Our work is based on self-training (e.g.,[59, 79, 56]). Astrophysical Observatory. For labeled images, we use a batch size of 2048 by default and reduce the batch size when we could not fit the model into the memory. Please 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 Copyright and all rights therein are retained by authors or by other copyright holders. We then use the teacher model to generate pseudo labels on unlabeled images. Noisy Student (B7, L2) means to use EfficientNet-B7 as the student and use our best model with 87.4% accuracy as the teacher model. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. Are labels required for improving adversarial robustness? 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. 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. Soft pseudo labels lead to better performance for low confidence data. augmentation, dropout, stochastic depth to the student so that the noised 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. Noisy Student Training is a semi-supervised learning approach. 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. 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. 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 It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Flip probability is the probability that the model changes top-1 prediction for different perturbations. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. Use Git or checkout with SVN using the web URL. With Noisy Student, the model correctly predicts dragonfly for the image. 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. 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. If nothing happens, download GitHub Desktop and try again. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. We apply dropout to the final classification layer with a dropout rate of 0.5. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. A. Krizhevsky, I. Sutskever, and G. E. Hinton, Temporal ensembling for semi-supervised learning, Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks, Workshop on Challenges in Representation Learning, ICML, Certainty-driven consistency loss for semi-supervised learning, C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, R. G. Lopes, D. Yin, B. Poole, J. Gilmer, and E. D. Cubuk, Improving robustness without sacrificing accuracy with patch gaussian augmentation, Y. Luo, J. Zhu, M. Li, Y. Ren, and B. Zhang, Smooth neighbors on teacher graphs for semi-supervised learning, L. Maale, C. K. Snderby, S. K. Snderby, and O. Winther, A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, Towards deep learning models resistant to adversarial attacks, D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten, Exploring the limits of weakly supervised pretraining, T. Miyato, S. Maeda, S. Ishii, and M. Koyama, Virtual adversarial training: a regularization method for supervised and semi-supervised learning, IEEE transactions on pattern analysis and machine intelligence, A. Najafi, S. Maeda, M. Koyama, and T. Miyato, Robustness to adversarial perturbations in learning from incomplete data, J. Ngiam, D. Peng, V. Vasudevan, S. Kornblith, Q. V. Le, and R. Pang, Robustness properties of facebooks resnext wsl models, Adversarial dropout for supervised and semi-supervised learning, Lessons from building acoustic models with a million hours of speech, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), S. Qiao, W. Shen, Z. Zhang, B. Wang, and A. Yuille, Deep co-training for semi-supervised image recognition, I. Radosavovic, P. Dollr, R. Girshick, G. Gkioxari, and K. He, Data distillation: towards omni-supervised learning, A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, Semi-supervised learning with ladder networks, E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, Proceedings of the AAAI Conference on Artificial Intelligence, B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. Noisy Student self-training is an effective way to leverage unlabelled datasets and improving accuracy by adding noise to the student model while training so it learns beyond the teacher's knowledge. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. During the generation of the pseudo Are you sure you want to create this branch? 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. Agreement NNX16AC86A, Is ADS down? When the student model is deliberately noised it is actually trained to be consistent to the more powerful teacher model that is not noised when it generates pseudo labels. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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. For simplicity, we experiment with using 1128,164,132,116,14 of the whole data by uniformly sampling images from the the unlabeled set though taking the images with highest confidence leads to better results. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. See 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.
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