Self-Training With Noisy Student Improves ImageNet Classification You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Here we study how to effectively use out-of-domain data. Distillation Survey : Noisy Student | 9to5Tutorial 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. Work fast with our official CLI. to use Codespaces. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . 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. 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. 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 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. We apply dropout to the final classification layer with a dropout rate of 0.5. 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. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. 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. We use the standard augmentation instead of RandAugment in this experiment. Self-training with Noisy Student improves ImageNet classification If nothing happens, download Xcode and try again. 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. During the generation of the pseudo 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. Summarization_self-training_with_noisy_student_improves_imagenet Self-Training With Noisy Student Improves ImageNet Classification @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019 . 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]. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Please 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. 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. Finally, in the above, we say that the pseudo labels can be soft or hard. The accuracy is improved by about 10% in most settings. Do imagenet classifiers generalize to imagenet? 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. Self-training with Noisy Student improves ImageNet classification For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Imaging, 39 (11) (2020), pp. In particular, we first perform normal training with a smaller resolution for 350 epochs. In other words, the student is forced to mimic a more powerful ensemble model. 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: Train a classifier on labeled data (teacher). CLIP: Connecting text and images - OpenAI Le. Hence we use soft pseudo labels for our experiments unless otherwise specified. 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. student is forced to learn harder from the pseudo labels. 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. 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). A tag already exists with the provided branch name. 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. 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]. (using extra training data). 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. 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. and surprising gains on robustness and adversarial benchmarks. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. 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. We use a resolution of 800x800 in this experiment. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. However an important requirement for Noisy Student to work well is that the student model needs to be sufficiently large to fit more data (labeled and pseudo labeled). Noisy Student Training is a semi-supervised learning approach. 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. 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. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. 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. 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. Finally, we iterate the algorithm a few times by treating the student as a teacher to generate new pseudo labels and train a new student. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 We also study the effects of using different amounts of unlabeled data. We use the same architecture for the teacher and the student and do not perform iterative training. 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. Noisy StudentImageNetEfficientNet-L2state-of-the-art. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network.
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self training with noisy student improves imagenet classification