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. Code for Noisy Student Training. The results are shown in Figure 4 with the following observations: (1) Soft pseudo labels and hard pseudo labels can both lead to great improvements with in-domain unlabeled images i.e., high-confidence images. Noisy Students performance improves with more unlabeled data. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. The abundance of data on the internet is vast. Noise Self-training with Noisy Student 1. 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. 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. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. For RandAugment, we apply two random operations with the magnitude set to 27. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. Self-Training With Noisy Student Improves ImageNet Classification The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. In terms of methodology, It implements SemiSupervised Learning with Noise to create an Image Classification. This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. 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. However, manually annotating organs from CT scans is time . We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Our study shows that using unlabeled data improves accuracy and general robustness. With Noisy Student, the model correctly predicts dragonfly for the image. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. self-mentoring outperforms data augmentation and self training. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. Self-training with Noisy Student improves ImageNet classification Please refer to [24] for details about mCE and AlexNets error rate. 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. 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. https://arxiv.org/abs/1911.04252. 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. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. Learn more. Train a classifier on labeled data (teacher). For simplicity, we experiment with using 1128,164,132,116,14 of the whole data by uniformly sampling images from the the unlabeled set though taking the images with highest confidence leads to better results. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. 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. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. 10687-10698). supervised model from 97.9% accuracy to 98.6% accuracy. Papers With Code is a free resource with all data licensed under. 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. There was a problem preparing your codespace, please try again. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. Iterative training is not used here for simplicity. It is expensive and must be done with great care. augmentation, dropout, stochastic depth to the student so that the noised Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. 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 call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. (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. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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. When the student model is deliberately noised it is actually trained to be consistent to the more powerful teacher model that is not noised when it generates pseudo labels. This material is presented to ensure timely dissemination of scholarly and technical work. 27.8 to 16.1. Self-Training With Noisy Student Improves ImageNet Classification It can be seen that masks are useful in improving classification performance. Especially unlabeled images are plentiful and can be collected with ease. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Noisy Student can still improve the accuracy to 1.6%. The baseline model achieves an accuracy of 83.2. 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]. We use stochastic depth[29], dropout[63] and RandAugment[14]. The width. Self-training with Noisy Student improves ImageNet classification These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Self-training with Noisy Student improves ImageNet classification tsai - Noisy student Do better imagenet models transfer better? Are you sure you want to create this branch? 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. 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). Proceedings of the eleventh annual conference on Computational learning theory, Proceedings of the IEEE conference on computer vision and pattern recognition, Empirical Methods in Natural Language Processing (EMNLP), Imagenet classification with deep convolutional neural networks, Domain adaptive transfer learning with specialist models, Thirty-Second AAAI Conference on Artificial Intelligence, Regularized evolution for image classifier architecture search, Inception-v4, inception-resnet and the impact of residual connections on learning. On . The results also confirm that vision models can benefit from Noisy Student even without iterative training. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. This is probably because it is harder to overfit the large unlabeled dataset. 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. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. 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. Noisy Student leads to significant improvements across all model sizes for EfficientNet. 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]. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. Our main results are shown in Table1. Self-Training With Noisy Student Improves ImageNet Classification Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. In other words, the student is forced to mimic a more powerful ensemble model. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Here we study how to effectively use out-of-domain data. Self-Training Noisy Student " " Self-Training . In other words, small changes in the input image can cause large changes to the predictions. Learn more. Code for Noisy Student Training. over the JFT dataset to predict a label for each image. The performance consistently drops with noise function removed. 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. Work fast with our official CLI. If nothing happens, download GitHub Desktop and try again. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Different types of. For more information about the large architectures, please refer to Table7 in Appendix A.1. This invariance constraint reduces the degrees of freedom in the model. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. 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. 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. 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. Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Use Git or checkout with SVN using the web URL. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. w Summary of key results compared to previous state-of-the-art models. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. . See Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. To achieve this result, we first train an EfficientNet model on labeled Their main goal is to find a small and fast model for deployment. Code is available at https://github.com/google-research/noisystudent. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. A common workaround is to use entropy minimization or ramp up the consistency loss. 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. 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. The inputs to the algorithm are both labeled and unlabeled images. Zoph et al. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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. We present a simple self-training method that achieves 87.4 possible. 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. Use Git or checkout with SVN using the web URL. task. PDF Self-Training with Noisy Student Improves ImageNet Classification 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. 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. Different kinds of noise, however, may have different effects. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . Noisy Student Training seeks to improve on self-training and distillation in two ways. Self-training with Noisy Student improves ImageNet classification. ImageNet . on ImageNet ReaL Although noise may appear to be limited and uninteresting, when it is applied to unlabeled data, it has a compound benefit of enforcing local smoothness in the decision function on both labeled and unlabeled data. We iterate this process by putting back the student as the teacher. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. On robustness test sets, it improves ImageNet-A top . Do imagenet classifiers generalize to imagenet? 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. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. We then train a larger EfficientNet as a student model on the Train a larger classifier on the combined set, adding noise (noisy student). IEEE Trans. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. We iterate this process by putting back the student as the teacher. Self-Training With Noisy Student Improves ImageNet Classification on ImageNet, which is 1.0 During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. [68, 24, 55, 22]. Self-training with noisy student improves imagenet classification. 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 [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. For classes where we have too many images, we take the images with the highest confidence. 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. Our procedure went as follows. Infer labels on a much larger unlabeled dataset. Further, Noisy Student outperforms the state-of-the-art accuracy of 86.4% by FixRes ResNeXt-101 WSL[44, 71] that requires 3.5 Billion Instagram images labeled with tags. 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. Soft pseudo labels lead to better performance for low confidence data. GitHub - google-research/noisystudent: Code for Noisy Student Training 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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.