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

Use, Smithsonian [57] used self-training for domain adaptation. But during the learning of the student, we inject noise such as data 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. . IEEE Transactions on Pattern Analysis and Machine Intelligence. 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. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. It is found that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. This invariance constraint reduces the degrees of freedom in the model. Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. Iterative training is not used here for simplicity. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model We iterate this process by putting back the student as the teacher. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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. 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. Add a Self-Training : Noisy Student : 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 . Noisy StudentImageNetEfficientNet-L2state-of-the-art. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Self-Training With Noisy Student Improves ImageNet Classification We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. 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. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). For each class, we select at most 130K images that have the highest confidence. The accuracy is improved by about 10% in most settings. This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. Train a larger classifier on the combined set, adding noise (noisy student). Self-training with Noisy Student improves ImageNet classification A semi-supervised segmentation network based on noisy student learning 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]). During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. Work fast with our official CLI. For instance, on the right column, as the image of the car undergone a small rotation, the standard model changes its prediction from racing car to car wheel to fire engine. We apply dropout to the final classification layer with a dropout rate of 0.5. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a On . ImageNet . Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Their noise model is video specific and not relevant for image classification. You signed in with another tab or window. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). Self-training with Noisy Student improves ImageNet classification 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. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. Semi-supervised medical image classification with relation-driven self-ensembling model. It is expensive and must be done with great care. Our work is based on self-training (e.g.,[59, 79, 56]). If nothing happens, download Xcode and try again. International Conference on Machine Learning, Learning extraction patterns for subjective expressions, Proceedings of the 2003 conference on Empirical methods in natural language processing, A. Roy Chowdhury, P. Chakrabarty, A. Singh, S. Jin, H. Jiang, L. Cao, and E. G. Learned-Miller, Automatic adaptation of object detectors to new domains using self-training, T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, Probability of error of some adaptive pattern-recognition machines, W. Shi, Y. Gong, C. Ding, Z. MaXiaoyu Tao, and N. Zheng, Transductive semi-supervised deep learning using min-max features, C. Simon-Gabriel, Y. Ollivier, L. Bottou, B. Schlkopf, and D. Lopez-Paz, First-order adversarial vulnerability of neural networks and input dimension, Very deep convolutional networks for large-scale image recognition, N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. This material is presented to ensure timely dissemination of scholarly and technical work. Work fast with our official CLI. 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. 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. Self-training with Noisy Student improves ImageNet classification possible. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet The performance consistently drops with noise function removed. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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. We iterate this process by putting back the student as the teacher. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. Self-Training With Noisy Student Improves ImageNet Classification We then select images that have confidence of the label higher than 0.3. unlabeled images. You signed in with another tab or window. 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. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. We use EfficientNet-B0 as both the teacher model and the student model and compare using Noisy Student with soft pseudo labels and hard pseudo labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. We present a simple self-training method that achieves 87.4 Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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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 . 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. 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. After using the masks generated by teacher-SN, the classification performance improved by 0.2 of AC, 1.2 of SP, and 0.7 of AUC. CLIP: Connecting text and images - OpenAI Self-mentoring: : A new deep learning pipeline to train a self Self-training with Noisy Student improves ImageNet classification Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Yalniz et al. We use EfficientNet-B4 as both the teacher and the student. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Especially unlabeled images are plentiful and can be collected with ease. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. The results also confirm that vision models can benefit from Noisy Student even without iterative training. 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. 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. For RandAugment, we apply two random operations with the magnitude set to 27. Finally, in the above, we say that the pseudo labels can be soft or hard. . ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. We find that Noisy Student is better with an additional trick: data balancing. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We start with the 130M unlabeled images and gradually reduce the number of images. Their purpose is different from ours: to adapt a teacher model on one domain to another. 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. This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. Learn more. 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. Med. 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. 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. [68, 24, 55, 22]. 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]. 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. We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. See Self-training with Noisy Student improves ImageNet classification Note that these adversarial robustness results are not directly comparable to prior works since we use a large input resolution of 800x800 and adversarial vulnerability can scale with the input dimension[17, 20, 19, 61]. For more information about the large architectures, please refer to Table7 in Appendix A.1. During the generation of the pseudo 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.
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