self training with noisy student improves imagenet classification self training with noisy student improves imagenet classification

w Summary of key results compared to previous state-of-the-art models. The comparison is shown in Table 9. 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. Le. In typical self-training with the teacher-student framework, noise injection to the student is not used by default, or the role of noise is not fully understood or justified. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . We apply dropout to the final classification layer with a dropout rate of 0.5. Self-training 1 2Self-training 3 4n What is Noisy Student? Finally, in the above, we say that the pseudo labels can be soft or hard. 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 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 . 10687-10698). This paper reviews the state-of-the-art in both the field of CNNs for image classification and object detection and Autonomous Driving Systems (ADSs) in a synergetic way including a comprehensive trade-off analysis from a human-machine perspective. Models are available at this https URL. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. all 12, Image Classification We iterate this process by putting back the student as the teacher. 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. You signed in with another tab or window. 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. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled 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. 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). In other words, the student is forced to mimic a more powerful ensemble model. There was a problem preparing your codespace, please try again. We then select images that have confidence of the label higher than 0.3. possible. 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. You signed in with another tab or window. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Hence we use soft pseudo labels for our experiments unless otherwise specified. Ranked #14 on This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. Learn more. sign in The best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. Noisy Students performance improves with more unlabeled data. The learning rate starts at 0.128 for labeled batch size 2048 and decays by 0.97 every 2.4 epochs if trained for 350 epochs or every 4.8 epochs if trained for 700 epochs. 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]. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen, GPipe: efficient training of giant neural networks using pipeline parallelism, A. Iscen, G. Tolias, Y. Avrithis, and O. Train a classifier on labeled data (teacher). [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . We iterate this process by Semi-supervised medical image classification with relation-driven self-ensembling model. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). We improved it by adding noise to the student to learn beyond the teachers knowledge. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Code for Noisy Student Training. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. 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. Noisy Student Training is a semi-supervised learning approach. Add a 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). The accuracy is improved by about 10% in most settings. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. Although they have produced promising results, in our preliminary experiments, consistency regularization works less well on ImageNet because consistency regularization in the early phase of ImageNet training regularizes the model towards high entropy predictions, and prevents it from achieving good accuracy. Flip probability is the probability that the model changes top-1 prediction for different perturbations. IEEE Transactions on Pattern Analysis and Machine Intelligence. 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. The inputs to the algorithm are both labeled and unlabeled images. 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. 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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. Copyright and all rights therein are retained by authors or by other copyright holders. 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. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. A tag already exists with the provided branch name. We present a simple self-training method that achieves 87.4 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. Here we study how to effectively use out-of-domain data. putting back the student as the teacher. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. 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.