self training with noisy student improves imagenet classification

combination of labeled and pseudo labeled images. Self-Training with Noisy Student Improves ImageNet Classification We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Iterative training is not used here for simplicity. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. Computer Science - Computer Vision and Pattern Recognition. . We iterate this process by putting back the student as the teacher. Especially unlabeled images are plentiful and can be collected with ease. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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 call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 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. This material is presented to ensure timely dissemination of scholarly and technical work. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. 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. 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. Train a larger classifier on the combined set, adding noise (noisy student). Self-training 1 2Self-training 3 4n What is Noisy Student? Hence the total number of images that we use for training a student model is 130M (with some duplicated images). Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Self-training with Noisy Student. Whether the model benefits from more unlabeled data depends on the capacity of the model since a small model can easily saturate, while a larger model can benefit from more data. We then train a larger EfficientNet as a student model on the Models are available at this https URL. ImageNet images and use it as a teacher to generate pseudo labels on 300M 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. 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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. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Do imagenet classifiers generalize to imagenet? Please Our study shows that using unlabeled data improves accuracy and general robustness. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. This work proposes a novel architectural unit, which is term the Squeeze-and-Excitation (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and shows that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. Astrophysical Observatory. Zoph et al. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 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. For example, with all noise removed, the accuracy drops from 84.9% to 84.3% in the case with 130M unlabeled images and drops from 83.9% to 83.2% in the case with 1.3M unlabeled images. Self-training with Noisy Student improves ImageNet classification. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). We start with the 130M unlabeled images and gradually reduce the number of images. 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. Learn more. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. We then use the teacher model to generate pseudo labels on unlabeled images. A common workaround is to use entropy minimization or ramp up the consistency loss. The performance consistently drops with noise function removed. 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. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. on ImageNet, which is 1.0 on ImageNet ReaL Figure 1(b) shows images from ImageNet-C and the corresponding predictions. Especially unlabeled images are plentiful and can be collected with ease. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Notice, Smithsonian Terms of 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. Edit social preview. Please First, a teacher model is trained in a supervised fashion. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. 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. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. In particular, we first perform normal training with a smaller resolution for 350 epochs. Noisy Student Training seeks to improve on self-training and distillation in two ways. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. 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. putting back the student as the teacher. A tag already exists with the provided branch name. 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. all 12, Image Classification We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. 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. Papers With Code is a free resource with all data licensed under. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 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. 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. It implements SemiSupervised Learning with Noise to create an Image Classification. 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 The width. 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.

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