Use, Smithsonian We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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 . As noise injection methods are not used in the student model, and the student model was also small, it is more difficult to make the student better than teacher. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical The abundance of data on the internet is vast. For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. We start with the 130M unlabeled images and gradually reduce the number of images. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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 top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. We apply dropout to the final classification layer with a dropout rate of 0.5. combination of labeled and pseudo labeled images. 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 then use the teacher model to generate pseudo labels on unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. There was a problem preparing your codespace, please try again. 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. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Our experiments show that an important element for this simple method to work well at scale is that the student model should be noised during its training while the teacher should not be noised during the generation of pseudo labels. On, International journal of molecular sciences. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. We iterate this process by putting back the student as the teacher. Med. The width. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. For RandAugment, we apply two random operations with the magnitude set to 27. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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). The accuracy is improved by about 10% in most settings. 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. 27.8 to 16.1. We duplicate images in classes where there are not enough images. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. For more information about the large architectures, please refer to Table7 in Appendix A.1. 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. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. on ImageNet ReaL Train a larger classifier on the combined set, adding noise (noisy student). The most interesting image is shown on the right of the first row. 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]. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. On . With Noisy Student, the model correctly predicts dragonfly for the image. We use the same architecture for the teacher and the student and do not perform iterative training. To achieve this result, we first train an EfficientNet model on labeled Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). 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 iterate this process by putting back the student as the teacher. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. 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. 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 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. 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. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. 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. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. The comparison is shown in Table 9. 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. Self-training with Noisy Student. Noisy Student leads to significant improvements across all model sizes for EfficientNet. A number of studies, e.g. First, we run an EfficientNet-B0 trained on ImageNet[69]. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. 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. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. 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. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. We improved it by adding noise to the student to learn beyond the teachers knowledge. 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. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. 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]). 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. 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. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. Train a classifier on labeled data (teacher). Yalniz et al. We iterate this process by putting back the student as the teacher. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. Agreement NNX16AC86A, Is ADS down? student is forced to learn harder from the pseudo labels. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. In contrast, the predictions of the model with Noisy Student remain quite stable. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Noisy Students performance improves with more unlabeled data. 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]. In terms of methodology, On robustness test sets, it improves ImageNet-A top . (Submitted on 11 Nov 2019) We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. 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]. Self-training Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. labels, the teacher is not noised so that the pseudo labels are as good as Zoph et al. We iterate this process by putting back the student as the teacher. to use Codespaces. 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.

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