First, a teacher model is trained in a supervised fashion. 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]. 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 . Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). self-mentoring outperforms data augmentation and self training. Self-training with Noisy Student. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. 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. We iterate this process by putting back the student as the teacher. . We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 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. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Noisy Student can still improve the accuracy to 1.6%. We then use the teacher model to generate pseudo labels on unlabeled images. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. 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. 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. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. to use Codespaces. 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. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. The main difference between Data Distillation and our method is that we use the noise to weaken the student, which is the opposite of their approach of strengthening the teacher by ensembling. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Semi-supervised medical image classification with relation-driven self-ensembling model. We use the same architecture for the teacher and the student and do not perform iterative training. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. This invariance constraint reduces the degrees of freedom in the model. The width. We evaluate the best model, that achieves 87.4% top-1 accuracy, on three robustness test sets: ImageNet-A, ImageNet-C and ImageNet-P. ImageNet-C and P test sets[24] include images with common corruptions and perturbations such as blurring, fogging, rotation and scaling. If nothing happens, download GitHub Desktop and try again. 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. Test images on ImageNet-P underwent different scales of perturbations. Finally, in the above, we say that the pseudo labels can be soft or hard. Noisy Student leads to significant improvements across all model sizes for EfficientNet. 27.8 to 16.1. We determine number of training steps and the learning rate schedule by the batch size for labeled images. 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 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 model with Noisy Student can successfully predict the correct labels of these highly difficult images. 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Use Git or checkout with SVN using the web URL. In other words, the student is forced to mimic a more powerful ensemble model. 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. In contrast, the predictions of the model with Noisy Student remain quite stable. On, International journal of molecular sciences. In this section, we study the importance of noise and the effect of several noise methods used in our model. Learn more. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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]. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Unlike previous studies in semi-supervised learning that use in-domain unlabeled data (e.g, ., CIFAR-10 images as unlabeled data for a small CIFAR-10 training set), to improve ImageNet, we must use out-of-domain unlabeled data. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. task. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. The abundance of data on the internet is vast. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. (using extra training data). Noisy StudentImageNetEfficientNet-L2state-of-the-art. 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]. Are labels required for improving adversarial robustness? Le. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). 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 with Noisy Student improves ImageNet classification Abstract. 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. 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. We iterate this process by putting back the student as the teacher. putting back the student as the teacher. Hence we use soft pseudo labels for our experiments unless otherwise specified. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Noisy Students performance improves with more unlabeled data. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. During this process, we kept increasing the size of the student model to improve the performance. Different kinds of noise, however, may have different effects. labels, the teacher is not noised so that the pseudo labels are as good as 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. 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 biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. on ImageNet, which is 1.0 A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. We apply dropout to the final classification layer with a dropout rate of 0.5. As can be seen from Table 8, the performance stays similar when we reduce the data to 116 of the total data, which amounts to 8.1M images after duplicating. There was a problem preparing your codespace, please try again. We iterate this process by putting back the student as the teacher. Different types of. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. Self-training 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. Classification of Socio-Political Event Data, SLADE: A Self-Training Framework For Distance Metric Learning, Self-Training with Differentiable Teacher, https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. Noisy Student Training seeks to improve on self-training and distillation in two ways. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. In particular, we first perform normal training with a smaller resolution for 350 epochs. This material is presented to ensure timely dissemination of scholarly and technical work. We duplicate images in classes where there are not enough images. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. We will then show our results on ImageNet and compare them with state-of-the-art models. For classes where we have too many images, we take the images with the highest confidence. 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. We do not tune these hyperparameters extensively since our method is highly robust to them. It 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 accuracy is improved by about 10% in most settings. Especially unlabeled images are plentiful and can be collected with ease. We find that Noisy Student is better with an additional trick: data balancing. Imaging, 39 (11) (2020), pp. 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. For each class, we select at most 130K images that have the highest confidence. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative and surprising gains on robustness and adversarial benchmarks. 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 vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. 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. Parthasarathi et al. 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 . 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). The baseline model achieves an accuracy of 83.2. to noise the student. Due to duplications, there are only 81M unique images among these 130M images. These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. You signed in with another tab or window. Train a classifier on labeled data (teacher). 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 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. Use Git or checkout with SVN using the web URL. sign in 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. 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. Although they have produced promising results, in our preliminary experiments, consistency regularization works less well on ImageNet because consistency regularization in the early phase of ImageNet training regularizes the model towards high entropy predictions, and prevents it from achieving good accuracy. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Our study shows that using unlabeled data improves accuracy and general robustness. Ranked #14 on possible. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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. Please refer to [24] for details about mFR and AlexNets flip probability. 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. Our work is based on self-training (e.g.,[59, 79, 56]). Zoph et al. 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. 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 Since a teacher models confidence on an image can be a good indicator of whether it is an out-of-domain image, we consider the high-confidence images as in-domain images and the low-confidence images as out-of-domain images. It can be seen that masks are useful in improving classification performance. 3.5B weakly labeled Instagram 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%. . 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. For more information about the large architectures, please refer to Table7 in Appendix A.1. The comparison is shown in Table 9. A number of studies, e.g. augmentation, dropout, stochastic depth to the student so that the noised By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. all 12, Image Classification We improved it by adding noise to the student to learn beyond the teachers knowledge. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. The performance drops when we further reduce it. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. 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. But during the learning of the student, we inject noise such as data (or is it just me), Smithsonian Privacy This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Train a larger classifier on the combined set, adding noise (noisy student). We also study the effects of using different amounts of unlabeled data. In terms of methodology, Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Self-training with noisy student improves imagenet classification. 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. 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. supervised model from 97.9% accuracy to 98.6% 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. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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. 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. This model investigates a new method. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and Copyright and all rights therein are retained by authors or by other copyright holders. [68, 24, 55, 22]. The inputs to the algorithm are both labeled and unlabeled images. ; 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. 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. 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 study if it is possible to improve performance on small models by using a larger teacher model, since small models are useful when there are constraints for model size and latency in real-world applications. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . We use a resolution of 800x800 in this experiment. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 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. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. 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. 10687-10698 Abstract Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. 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. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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]. A tag already exists with the provided branch name. Self-training with Noisy Student improves ImageNet classification. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. 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. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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. 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. If nothing happens, download Xcode and try again. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. . It is expensive and must be done with great care. However, in the case with 130M unlabeled images, with noise function removed, the performance is still improved to 84.3% from 84.0% when compared to the supervised baseline.
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