1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model putting back the student as the teacher. 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. Self-training with Noisy Student improves ImageNet classification. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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. We find that Noisy Student is better with an additional trick: data balancing. 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. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Self-training A tag already exists with the provided branch name. 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. sign in First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Soft pseudo labels lead to better performance for low confidence data. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Do imagenet classifiers generalize to imagenet? Computer Science - Computer Vision and Pattern Recognition. 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. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. The algorithm is basically self-training, a method in semi-supervised learning (. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. Use, Smithsonian Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. (using extra training data). You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. 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. 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. Add a The model with Noisy Student can successfully predict the correct labels of these highly difficult images. It implements SemiSupervised Learning with Noise to create an Image Classification. ; 2006)[book reviews], Semi-supervised deep learning with memory, Proceedings of the European Conference on Computer Vision (ECCV), Xception: deep learning with depthwise separable convolutions, K. Clark, M. Luong, C. D. Manning, and Q. V. Le, Semi-supervised sequence modeling with cross-view training, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, AutoAugment: learning augmentation strategies from data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, RandAugment: practical data augmentation with no separate search, Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, Good semi-supervised learning that requires a bad gan, T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, A. Galloway, A. Golubeva, T. Tanay, M. Moussa, and G. W. Taylor, R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow, I. J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and harnessing adversarial examples, Semi-supervised learning by entropy minimization, Advances in neural information processing systems, K. Gu, B. Yang, J. Ngiam, Q. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. task. 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. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a on ImageNet ReaL. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. 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. 10687-10698). In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. 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. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Self-training with Noisy Student improves ImageNet classificationCVPR2020, Codehttps://github.com/google-research/noisystudent, Self-training, 1, 2Self-training, Self-trainingGoogleNoisy Student, Noisy Studentstudent modeldropout, stochastic depth andaugmentationteacher modelNoisy Noisy Student, Noisy Student, 1, JFT3ImageNetEfficientNet-B00.3130K130K, EfficientNetbaseline modelsEfficientNetresnet, EfficientNet-B7EfficientNet-L0L1L2, batchsize = 2048 51210242048EfficientNet-B4EfficientNet-L0l1L2350epoch700epoch, 2EfficientNet-B7EfficientNet-L0, 3EfficientNet-L0EfficientNet-L1L0, 4EfficientNet-L1EfficientNet-L2, student modelNoisy, noisystudent modelteacher modelNoisy, Noisy, Self-trainingaugmentationdropoutstochastic depth, Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores., 12/self-training-with-noisy-student-f33640edbab2, EfficientNet-L0EfficientNet-B7B7, EfficientNet-L1EfficientNet-L0, EfficientNetsEfficientNet-L1EfficientNet-L2EfficientNet-L2EfficientNet-B75. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. 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. to noise the student. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Please refer to [24] for details about mFR and AlexNets flip probability. As shown in Table3,4 and5, when compared with the previous state-of-the-art model ResNeXt-101 WSL[44, 48] trained on 3.5B weakly labeled images, Noisy Student yields substantial gains on robustness datasets. Code for Noisy Student Training. 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. (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. 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. 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. By clicking accept or continuing to use the site, you agree to the terms outlined in our. ImageNet-A top-1 accuracy from 16.6 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. We use the standard augmentation instead of RandAugment in this experiment. In other words, small changes in the input image can cause large changes to the predictions. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Med. We use EfficientNet-B4 as both the teacher and the student. 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. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 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 . The abundance of data on the internet is vast. This model investigates a new method. The baseline model achieves an accuracy of 83.2. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Self-training with Noisy Student. 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. 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. over the JFT dataset to predict a label for each image. Do better imagenet models transfer better? A. Alemi, Thirty-First AAAI Conference on Artificial Intelligence, C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, EfficientNet: rethinking model scaling for convolutional neural networks, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, A. Vedaldi, M. Douze, and H. Jgou, Fixing the train-test resolution discrepancy, V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), J. Weston, F. Ratle, H. Mobahi, and R. Collobert, Deep learning via semi-supervised embedding, Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le, Unsupervised data augmentation for consistency training, S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, I. Hence we use soft pseudo labels for our experiments unless otherwise specified. to use Codespaces. Train a larger classifier on the combined set, adding noise (noisy student). . Unlike previous studies in semi-supervised learning that use in-domain unlabeled data (e.g, ., CIFAR-10 images as unlabeled data for a small CIFAR-10 training set), to improve ImageNet, we must use out-of-domain unlabeled data. 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. Code is available at https://github.com/google-research/noisystudent. This material is presented to ensure timely dissemination of scholarly and technical work. As can be seen from the figure, our model with Noisy Student makes correct predictions for images under severe corruptions and perturbations such as snow, motion blur and fog, while the model without Noisy Student suffers greatly under these conditions. 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]). Noise Self-training with Noisy Student 1. For RandAugment, we apply two random operations with the magnitude set to 27. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. We improved it by adding noise to the student to learn beyond the teachers knowledge. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). 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. The performance consistently drops with noise function removed. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Their main goal is to find a small and fast model for deployment. 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. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. The architectures for the student and teacher models can be the same or different. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and combination of labeled and pseudo labeled images. But training robust supervised learning models is requires this step. [68, 24, 55, 22]. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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].
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