self training with noisy student improves imagenet classification

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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Work fast with our official CLI. (using extra training data). to noise the student. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. Please refer to [24] for details about mCE and AlexNets error rate. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. 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 way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. 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. 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. Please refer to [24] for details about mFR and AlexNets flip probability. Self-Training With Noisy Student Improves ImageNet Classification When data augmentation noise is used, the student must ensure that a translated image, for example, should have the same category with a non-translated image. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. Similar to[71], we fix the shallow layers during finetuning. 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. The abundance of data on the internet is vast. PDF Self-Training with Noisy Student Improves ImageNet Classification For example, without Noisy Student, the model predicts bullfrog for the image shown on the left of the second row, which might be resulted from the black lotus leaf on the water. supervised model from 97.9% accuracy to 98.6% accuracy. We use stochastic depth[29], dropout[63] and RandAugment[14]. Self-Training Noisy Student " " Self-Training . 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. During the generation of the pseudo Please Parthasarathi et al. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. 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. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. 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. Self-training with Noisy Student improves ImageNet classification In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. 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 this section, we study the importance of noise and the effect of several noise methods used in our model. Self-training with Noisy Student improves ImageNet classification Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet Self-training with Noisy Student improves ImageNet classification 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). Use Git or checkout with SVN using the web URL. 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 . 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. The performance drops when we further reduce it. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). 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. The inputs to the algorithm are both labeled and unlabeled images. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. First, we run an EfficientNet-B0 trained on ImageNet[69]. To achieve this result, we first train an EfficientNet model on labeled Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. 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. 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. 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. ; 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. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. 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. Train a larger classifier on the combined set, adding noise (noisy student). CLIP: Connecting text and images - OpenAI Noisy Student leads to significant improvements across all model sizes for EfficientNet. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. E. Arazo, D. Ortego, P. Albert, N. E. OConnor, and K. McGuinness, Pseudo-labeling and confirmation bias in deep semi-supervised learning, B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson, There are many consistent explanations of unlabeled data: why you should average, International Conference on Learning Representations, Advances in Neural Information Processing Systems, D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, MixMatch: a holistic approach to semi-supervised learning, Combining labeled and unlabeled data with co-training, C. Bucilu, R. Caruana, and A. Niculescu-Mizil, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Y. Carmon, A. Raghunathan, L. Schmidt, P. Liang, and J. C. Duchi, Unlabeled data improves adversarial robustness, Semi-supervised learning (chapelle, o. et al., eds. combination of labeled and pseudo labeled images. A tag already exists with the provided branch name. 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. 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]. 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. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). It is expensive and must be done with great care. Are you sure you want to create this branch? 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. Self-training with Noisy Student improves ImageNet classification 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. Work fast with our official CLI. 27.8 to 16.1. 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. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture.

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