self training with noisy student improves imagenet classification

As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. 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. 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. A novel random matrix theory based damping learner for second order optimisers inspired by linear shrinkage estimation is developed, and it is demonstrated that the derived method works well with adaptive gradient methods such as Adam. ImageNet . On robustness test sets, it improves ImageNet-A top . 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. 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. Notice, Smithsonian Terms of Code for Noisy Student Training. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. First, a teacher model is trained in a supervised fashion. This material is presented to ensure timely dissemination of scholarly and technical work. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. 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. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Especially unlabeled images are plentiful and can be collected with ease. For each class, we select at most 130K images that have the highest confidence. Our study shows that using unlabeled data improves accuracy and general robustness. The mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. team using this approach not only surpasses the top-1 ImageNet accuracy of SOTA models by 1%, it also shows that the robustness of a model also improves. 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 . 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. You signed in with another tab or window. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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. We then train a larger EfficientNet as a student model on the As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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 Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. We duplicate images in classes where there are not enough images. The results also confirm that vision models can benefit from Noisy Student even without iterative training. If nothing happens, download GitHub Desktop and try again. Hence we use soft pseudo labels for our experiments unless otherwise specified. Self-training with Noisy Student improves ImageNet classification. When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single model. Significantly, after using the masks generated by student-SN, the classification performance improved by 0.9 of AC, 0.7 of SE, and 0.9 of AUC. Please Semi-supervised medical image classification with relation-driven self-ensembling model. Train a classifier on labeled data (teacher). We iterate this process by putting back the student as the teacher. Papers With Code is a free resource with all data licensed under. 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. (or is it just me), Smithsonian Privacy Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. 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. 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. 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. As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. . There was a problem preparing your codespace, please try again. 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. unlabeled 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. It is expensive and must be done with great care. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. A number of studies, e.g. 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. Do imagenet classifiers generalize to imagenet? Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). Self-training with noisy student improves imagenet classification. This model investigates a new method. 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 first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. If nothing happens, download Xcode and try again. In particular, we set the survival probability in stochastic depth to 0.8 for the final layer and follow the linear decay rule for other layers. Noisy Student leads to significant improvements across all model sizes for EfficientNet. 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. 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. Learn more. 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. 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. See A tag already exists with the provided branch name. Self-Training With Noisy Student Improves ImageNet Classification. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. self-mentoring outperforms data augmentation and self training. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. 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. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Use Git or checkout with SVN using the web URL. Yalniz et al. Please refer to [24] for details about mCE and AlexNets error rate. [68, 24, 55, 22]. 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. Different types of. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Agreement NNX16AC86A, Is ADS down? 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]. Learn more. A tag already exists with the provided branch name. We iterate this process by putting back the student as the teacher. Noisy Student Training seeks to improve on self-training and distillation in two ways. Le. Similar to[71], we fix the shallow layers during finetuning. Astrophysical Observatory. and surprising gains on robustness and adversarial benchmarks. Ranked #14 on We iterate this process by putting back the student as the teacher. We use the labeled images to train a teacher model using the standard cross entropy loss.

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