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

However, manually annotating organs from CT scans is time . If nothing happens, download Xcode and try again. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. to use Codespaces. 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. EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. A common workaround is to use entropy minimization or ramp up the consistency loss. 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. During the generation of the pseudo The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 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 . Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. In the following, we will first describe experiment details to achieve our results. over the JFT dataset to predict a label for each image. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. ImageNet-A top-1 accuracy from 16.6 We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. 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. 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. Here we show the evidence in Table 6, noise such as stochastic depth, dropout and data augmentation plays an important role in enabling the student model to perform better than the teacher. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. . all 12, Image Classification For labeled images, we use a batch size of 2048 by default and reduce the batch size when we could not fit the model into the memory. 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. 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. 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. 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. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. The paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task is revisited, and a simple recipe that is called Big Transfer (BiT) is created, which achieves strong performance on over 20 datasets. This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. 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. 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. 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. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. For RandAugment, we apply two random operations with the magnitude set to 27. w Summary of key results compared to previous state-of-the-art models. It is experimentally validated that, for a target test resolution, using a lower train resolution offers better classification at test time, and a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ is proposed. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. It 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. unlabeled images. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. 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. 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. 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. IEEE Trans. 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]. Are labels required for improving adversarial robustness? We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Agreement NNX16AC86A, Is ADS down? 10687-10698 Abstract 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]. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. Train a classifier on labeled data (teacher). Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Ranked #14 on This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. The most interesting image is shown on the right of the first row. 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. 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. You signed in with another tab or window. Then, that teacher is used to label the unlabeled data. For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. Do imagenet classifiers generalize to imagenet? Self-training with Noisy Student - Medium Figure 1(a) shows example images from ImageNet-A and the predictions of our models. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. This is probably because it is harder to overfit the large unlabeled dataset. 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. To achieve this result, we first train an EfficientNet model on labeled on ImageNet ReaL. Here we study how to effectively use out-of-domain data. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. For unlabeled images, we set the batch size to be three times the batch size of labeled images for large models, including EfficientNet-B7, L0, L1 and L2. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. team using this approach not only surpasses the top-1 ImageNet accuracy of SOTA models by 1%, it also shows that the robustness of a model also improves. We determine number of training steps and the learning rate schedule by the batch size for labeled images. Self-mentoring: : A new deep learning pipeline to train a self 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. It is expensive and must be done with great care. 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. The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. - : self-training_with_noisy_student_improves_imagenet_classification 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. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. 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). Le. The abundance of data on the internet is vast. putting back the student as the teacher. 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. Self-Training with Noisy Student Improves ImageNet Classification Self-training with Noisy Student improves ImageNet classification Why Self-training with Noisy Students beats SOTA Image classification For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. Finally, in the above, we say that the pseudo labels can be soft or hard. Please 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. 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. 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. Please refer to [24] for details about mFR and AlexNets flip probability. We use the standard augmentation instead of RandAugment in this experiment. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a sign in Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. We sample 1.3M images in confidence intervals. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. on ImageNet ReaL Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. During this process, we kept increasing the size of the student model to improve the performance. self-mentoring outperforms data augmentation and self training. (using extra training data). It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. If nothing happens, download Xcode and try again. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. Self-training with Noisy Student improves ImageNet classification This work proposes a novel architectural unit, which is term the Squeeze-and-Excitation (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and shows that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. to use Codespaces. A number of studies, e.g. IEEE Transactions on Pattern Analysis and Machine Intelligence. CVPR 2020 Open Access Repository Noisy Student Training is a semi-supervised learning approach. student is forced to learn harder from the pseudo labels. This material is presented to ensure timely dissemination of scholarly and technical work. Astrophysical Observatory.