Here we study how to effectively use out-of-domain data. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). Not only our method improves standard ImageNet accuracy, it also improves classification robustness on much harder test sets by large margins: ImageNet-A[25] top-1 accuracy from 16.6% to 74.2%, ImageNet-C[24] mean corruption error (mCE) from 45.7 to 31.2 and ImageNet-P[24] mean flip rate (mFR) from 27.8 to 16.1. We start with the 130M unlabeled images and gradually reduce the number of images. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. 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. We use the same architecture for the teacher and the student and do not perform iterative training. 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 is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. 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 performance drops when we further reduce it. 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. During this process, we kept increasing the size of the student model to improve the performance. Self-Training Noisy Student " " Self-Training . . In other words, the student is forced to mimic a more powerful ensemble model. Their main goal is to find a small and fast model for deployment. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. Self-training 1 2Self-training 3 4n What is Noisy Student? [57] used self-training for domain adaptation. 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. 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. We do not tune these hyperparameters extensively since our method is highly robust to them. - : self-training_with_noisy_student_improves_imagenet_classification Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Computer Science - Computer Vision and Pattern Recognition. 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 find that Noisy Student is better with an additional trick: data balancing. 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. et al. Since we use soft pseudo labels generated from the teacher model, when the student is trained to be exactly the same as the teacher model, the cross entropy loss on unlabeled data would be zero and the training signal would vanish. Please refer to [24] for details about mFR and AlexNets flip probability. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a The baseline model achieves an accuracy of 83.2. 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. Our study shows that using unlabeled data improves accuracy and general robustness. At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. In the following, we will first describe experiment details to achieve our results. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 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. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Med. Self-training with Noisy Student improves ImageNet classification Original paper: https://arxiv.org/pdf/1911.04252.pdf Authors: Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le HOYA012 Introduction EfficientNet ImageNet SOTA EfficientNet To date (2020) we will introduce "Noisy Student Training", which is a state-of-the-art model.The idea is to extend self-training and Distillation, a paper that shows that by adding three noises and distilling multiple times, the student model will have better generalization performance than the teacher model. . 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. Please refer to [24] for details about mCE and AlexNets error rate. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. Infer labels on a much larger unlabeled dataset. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet The main difference between Data Distillation and our method is that we use the noise to weaken the student, which is the opposite of their approach of strengthening the teacher by ensembling. 10687-10698 Abstract Summarization_self-training_with_noisy_student_improves_imagenet_classification. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. It implements SemiSupervised Learning with Noise to create an Image Classification. Semi-supervised medical image classification with relation-driven self-ensembling model. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. But during the learning of the student, we inject noise such as data 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 each class, we select at most 130K images that have the highest confidence. 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. 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. on ImageNet ReaL Papers With Code is a free resource with all data licensed under. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. We then perform data filtering and balancing on this corpus. The inputs to the algorithm are both labeled and unlabeled images. unlabeled images , . Are you sure you want to create this branch? We duplicate images in classes where there are not enough 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. task. 3.5B weakly labeled Instagram images. For instance, on ImageNet-A, Noisy Student achieves 74.2% top-1 accuracy which is approximately 57% more accurate than the previous state-of-the-art model. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. 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. 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. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Edit social preview. 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. In other words, small changes in the input image can cause large changes to the predictions. Self-Training With Noisy Student Improves ImageNet Classification. Please 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. IEEE Trans. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images.
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