We apply dropout to the final classification layer with a dropout rate of 0.5. We duplicate images in classes where there are not enough images. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. We use the same architecture for the teacher and the student and do not perform iterative training. Infer labels on a much larger unlabeled dataset. Astrophysical Observatory. 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. Self-training with Noisy Student improves ImageNet classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. self-mentoring outperforms data augmentation and self training. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. On robustness test sets, it improves EfficientNet with Noisy Student produces correct top-1 predictions (shown in. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Please refer to [24] for details about mCE and AlexNets error rate. For each class, we select at most 130K images that have the highest confidence. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. In the following, we will first describe experiment details to achieve our results. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from Here we study how to effectively use out-of-domain data. Soft pseudo labels lead to better performance for low confidence data. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. Code for Noisy Student Training. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. Use Git or checkout with SVN using the web URL. Our procedure went as follows. Self-Training Noisy Student " " Self-Training . 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. Zoph et al. Hence we use soft pseudo labels for our experiments unless otherwise specified. 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 obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. Flip probability is the probability that the model changes top-1 prediction for different perturbations. Agreement NNX16AC86A, Is ADS down? Noisy Student Training is a semi-supervised learning approach. As can be seen from the figure, our model with Noisy Student makes correct predictions for images under severe corruptions and perturbations such as snow, motion blur and fog, while the model without Noisy Student suffers greatly under these conditions. to noise the student. Papers With Code is a free resource with all data licensed under. 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 find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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]). Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We then perform data filtering and balancing on this corpus. Learn more. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. 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. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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. putting back the student as the teacher. 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. Use, Smithsonian 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model We iterate this process by putting back the student as the teacher. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. 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 is probably because it is harder to overfit the large unlabeled dataset. The baseline model achieves an accuracy of 83.2. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. Our 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. 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%. labels, the teacher is not noised so that the pseudo labels are as good as The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Please refer to [24] for details about mFR and AlexNets flip probability. 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. 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. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. . We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The accuracy is improved by about 10% in most settings. Please 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. 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 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. Their noise model is video specific and not relevant for image classification. (or is it just me), Smithsonian Privacy Self-training with Noisy Student improves ImageNet classification. This invariance constraint reduces the degrees of freedom in the model. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. 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. 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. 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. 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. 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). 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. The most interesting image is shown on the right of the first row. 3429-3440. . and surprising gains on robustness and adversarial benchmarks. During this process, we kept increasing the size of the student model to improve the performance. Edit social preview. 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. If nothing happens, download Xcode and try again. ImageNet . We iterate this process by putting back the student as the teacher. 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 . 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. possible. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. 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. 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. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). 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. . On, International journal of molecular sciences. Finally, in the above, we say that the pseudo labels can be soft or hard. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Parthasarathi et al. In particular, we first perform normal training with a smaller resolution for 350 epochs. Similar to[71], we fix the shallow layers during finetuning. On robustness test sets, it improves ImageNet-A top . 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]. Noise Self-training with Noisy Student 1. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Especially unlabeled images are plentiful and can be collected with ease. The performance consistently drops with noise function removed. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . 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. We use a resolution of 800x800 in this experiment. We also list EfficientNet-B7 as a reference. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Are you sure you want to create this branch? on ImageNet, which is 1.0 (using extra training data). 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. Chowdhury et al. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 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. 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. 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. over the JFT dataset to predict a label for each image. w Summary of key results compared to previous state-of-the-art models. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Due to duplications, there are only 81M unique images among these 130M images. In other words, the student is forced to mimic a more powerful ensemble model. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. We improved it by adding noise to the student to learn beyond the teachers knowledge. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. 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 this section, we study the importance of noise and the effect of several noise methods used in our model. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. 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. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. IEEE Trans. 10687-10698). The width. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. 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. on ImageNet ReaL. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. 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. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. We find that Noisy Student is better with an additional trick: data balancing. . Self-training with Noisy Student improves ImageNet classification. Work fast with our official CLI. 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. 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. 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. Are you sure you want to create this branch? ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Please [^reference-9] [^reference-10] A critical insight was to . After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. If nothing happens, download GitHub Desktop and try again. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Copyright and all rights therein are retained by authors or by other copyright holders. Code for Noisy Student Training. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. Self-training with Noisy Student. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. The inputs to the algorithm are both labeled and unlabeled images. In terms of methodology, [57] used self-training for domain adaptation. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. We also study the effects of using different amounts of unlabeled data. We use the standard augmentation instead of RandAugment in this experiment. 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. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. sign in We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. all 12, Image Classification 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. 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. Learn more. 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. 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. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. 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. 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 We use the labeled images to train a teacher model using the standard cross entropy loss. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on 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. 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.
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