Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. 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 . supervised model from 97.9% accuracy to 98.6% accuracy. 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 iterate this process by putting back the student as the teacher. 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. Train a larger classifier on the combined set, adding noise (noisy student). Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Flip probability is the probability that the model changes top-1 prediction for different perturbations. Iterative training is not used here for simplicity. student is forced to learn harder from the pseudo labels. all 12, Image Classification Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. 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. 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. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Code for Noisy Student Training. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin unlabeled images. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. 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. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. 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 purpose is different from ours: to adapt a teacher model on one domain to another. . Abdominal organ segmentation is very important for clinical applications. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. Self-training with Noisy Student improves ImageNet classification Self-Training With Noisy Student Improves ImageNet Classification In other words, the student is forced to mimic a more powerful ensemble model. (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. 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. 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. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). In contrast, the predictions of the model with Noisy Student remain quite stable. Self-training with Noisy Student improves ImageNet classication Qizhe Xie 1, Minh-Thang Luong , Eduard Hovy2, Quoc V. Le1 1Google Research, Brain Team, 2Carnegie Mellon University fqizhex, thangluong, [email protected], [email protected] Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . tsai - Noisy student Here we study if it is possible to improve performance on small models by using a larger teacher model, since small models are useful when there are constraints for model size and latency in real-world applications. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. 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. We determine number of training steps and the learning rate schedule by the batch size for labeled images. By showing the models only labeled images, we limit ourselves from making use of unlabeled images available in much larger quantities to improve accuracy and robustness of state-of-the-art models. Ranked #14 on CLIP (Contrastive Language-Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.The idea of zero-data learning dates back over a decade [^reference-8] but until recently was mostly studied in computer vision as a way of generalizing to unseen object categories. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. 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 As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. We then perform data filtering and balancing on this corpus. 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. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. A semi-supervised segmentation network based on noisy student learning . combination of labeled and pseudo labeled images. Our procedure went as follows. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Distillation Survey : Noisy Student | 9to5Tutorial . Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). We present a simple self-training method that achieves 87.4 We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We also list EfficientNet-B7 as a reference. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. We apply dropout to the final classification layer with a dropout rate of 0.5. Self-Training With Noisy Student Improves ImageNet Classification Similar to[71], we fix the shallow layers during finetuning. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. 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 Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. 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.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. 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. 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]. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Noise Self-training with Noisy Student 1. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. We improved it by adding noise to the student to learn beyond the teachers knowledge. Their main goal is to find a small and fast model for deployment. Noisy Student (EfficientNet) - huggingface.co 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. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. First, a teacher model is trained in a supervised fashion. Self-training with Noisy Student - Medium 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. over the JFT dataset to predict a label for each image. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. Infer labels on a much larger unlabeled dataset. A. Krizhevsky, I. Sutskever, and G. E. Hinton, Temporal ensembling for semi-supervised learning, Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks, Workshop on Challenges in Representation Learning, ICML, Certainty-driven consistency loss for semi-supervised learning, C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, R. G. Lopes, D. Yin, B. Poole, J. Gilmer, and E. D. Cubuk, Improving robustness without sacrificing accuracy with patch gaussian augmentation, Y. Luo, J. Zhu, M. Li, Y. Ren, and B. Zhang, Smooth neighbors on teacher graphs for semi-supervised learning, L. Maale, C. K. Snderby, S. K. Snderby, and O. Winther, A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, Towards deep learning models resistant to adversarial attacks, D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten, Exploring the limits of weakly supervised pretraining, T. Miyato, S. Maeda, S. 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Aggarwal, Y. Huang, and Q. V. Le, Proceedings of the AAAI Conference on Artificial Intelligence, B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet We also study the effects of using different amounts of unlabeled data. During this process, we kept increasing the size of the student model to improve the performance. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . We use the labeled images to train a teacher model using the standard cross entropy loss. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. , have shown that computer vision models lack robustness. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. Do better imagenet models transfer better? This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. Self-Training With Noisy Student Improves ImageNet Classification 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. 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. 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. If nothing happens, download GitHub Desktop and try again. Self-Training for Natural Language Understanding! The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. Then, that teacher is used to label the unlabeled data. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%.