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. Self-training was previously used to improve ResNet-50 from 76.4% to 81.2% top-1 accuracy[76] which is still far from the state-of-the-art accuracy. A tag already exists with the provided branch name. EfficientNet with Noisy Student produces correct top-1 predictions (shown 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. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. Semi-supervised medical image classification with relation-driven self-ensembling model. Learn more. Self-training with Noisy Student improves ImageNet classification 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. Noisy Student (EfficientNet) - huggingface.co We will then show our results on ImageNet and compare them with state-of-the-art models. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 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. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. Are labels required for improving adversarial robustness? These CVPR 2020 papers are the Open Access versions, provided by the. By clicking accept or continuing to use the site, you agree to the terms outlined in our. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. 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. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Here we study how to effectively use out-of-domain data. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. Self-training 1 2Self-training 3 4n What is Noisy Student? Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards putting back the student as the teacher. 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. Whether the model benefits from more unlabeled data depends on the capacity of the model since a small model can easily saturate, while a larger model can benefit from more data. Why Self-training with Noisy Students beats SOTA Image classification In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. 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. Self-training with Noisy Student - Self-training with Noisy Student improves ImageNet classification. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. on ImageNet ReaL Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. In other words, the student is forced to mimic a more powerful ensemble model. Train a classifier on labeled data (teacher). Med. on ImageNet, which is 1.0 We also list EfficientNet-B7 as a reference. 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. The comparison is shown in Table 9. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. If nothing happens, download Xcode and try again. 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. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Self-Training With Noisy Student Improves ImageNet Classification and surprising gains on robustness and adversarial benchmarks. For RandAugment, we apply two random operations with the magnitude set to 27. 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. 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. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Distillation Survey : Noisy Student | 9to5Tutorial 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. 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]. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Flip probability is the probability that the model changes top-1 prediction for different perturbations. Self-training first uses labeled data to train a good teacher model, then use the teacher model to label unlabeled data and finally use the labeled data and unlabeled data to jointly train a student model. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical 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. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. With Noisy Student, the model correctly predicts dragonfly for the image. unlabeled images , . 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]. We use the labeled images to train a teacher model using the standard cross entropy loss. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. For each class, we select at most 130K images that have the highest confidence. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images To achieve this result, we first train an EfficientNet model on labeled Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. Noisy Student Training is a semi-supervised learning approach. Astrophysical Observatory. Self-training with Noisy Student - Medium 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. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Self-Training Noisy Student " " Self-Training . Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. . Noisy StudentImageNetEfficientNet-L2state-of-the-art. We iterate this process by putting back the student as the teacher. It implements SemiSupervised Learning with Noise to create an Image Classification. 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. to use Codespaces. [^reference-9] [^reference-10] A critical insight was to . 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. 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. tsai - Noisy student 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 determine number of training steps and the learning rate schedule by the batch size for labeled images. Papers With Code is a free resource with all data licensed under. Self-Training With Noisy Student Improves ImageNet Classification 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. During this process, we kept increasing the size of the student model to improve the performance. 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. We start with the 130M unlabeled images and gradually reduce the number of images. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). There was a problem preparing your codespace, please try again. Do imagenet classifiers generalize to imagenet? The results also confirm that vision models can benefit from Noisy Student even without iterative training. 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. Do better imagenet models transfer better? Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. In particular, we set the survival probability in stochastic depth to 0.8 for the final layer and follow the linear decay rule for other layers. A number of studies, e.g. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 labels, the teacher is not noised so that the pseudo labels are as good as The ONCE (One millioN sCenEs) dataset for 3D object detection in the autonomous driving scenario is introduced and a benchmark is provided in which a variety of self-supervised and semi- supervised methods on the ONCE dataset are evaluated. Self-Training With Noisy Student Improves ImageNet Classification Self-training with Noisy Student improves ImageNet classification - : self-training_with_noisy_student_improves_imagenet_classification For example, with all noise removed, the accuracy drops from 84.9% to 84.3% in the case with 130M unlabeled images and drops from 83.9% to 83.2% in the case with 1.3M unlabeled images. We use EfficientNet-B4 as both the teacher and the student. [68, 24, 55, 22]. 10687-10698 Abstract 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 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. Self-Training With Noisy Student Improves ImageNet Classification We improved it by adding noise to the student to learn beyond the teachers knowledge. to use Codespaces. 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. Soft pseudo labels lead to better performance for low confidence data. Self-training with noisy student improves imagenet classification. 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. E. Arazo, D. Ortego, P. Albert, N. E. OConnor, and K. McGuinness, Pseudo-labeling and confirmation bias in deep semi-supervised learning, B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson, There are many consistent explanations of unlabeled data: why you should average, International Conference on Learning Representations, Advances in Neural Information Processing Systems, D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, MixMatch: a holistic approach to semi-supervised learning, Combining labeled and unlabeled data with co-training, C. Bucilu, R. Caruana, and A. Niculescu-Mizil, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Y. Carmon, A. Raghunathan, L. Schmidt, P. Liang, and J. C. Duchi, Unlabeled data improves adversarial robustness, Semi-supervised learning (chapelle, o. et al., eds. Different kinds of noise, however, may have different effects. 3429-3440. . The proposed use of distillation to only handle easy instances allows for a more aggressive trade-off in the student size, thereby reducing the amortized cost of inference and achieving better accuracy than standard distillation. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. This is probably because it is harder to overfit the large unlabeled dataset. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. In contrast, the predictions of the model with Noisy Student remain quite stable. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. w Summary of key results compared to previous state-of-the-art models. 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. Self-training 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. 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. The performance consistently drops with noise function removed. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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 iterate this process by putting back the student as the teacher. Since a teacher models confidence on an image can be a good indicator of whether it is an out-of-domain image, we consider the high-confidence images as in-domain images and the low-confidence images as out-of-domain images. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. 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. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 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]. ImageNet-A top-1 accuracy from 16.6 augmentation, dropout, stochastic depth to the student so that the noised The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. Notice, Smithsonian Terms of 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. Are you sure you want to create this branch? Noisy Student Training is a semi-supervised learning method which achieves 88.4% top-1 accuracy on ImageNet (SOTA) and surprising gains on robustness and adversarial benchmarks. Different types of. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. 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. Ishii, and M. Koyama, Virtual adversarial training: a regularization method for supervised and semi-supervised learning, IEEE transactions on pattern analysis and machine intelligence, A. Najafi, S. Maeda, M. Koyama, and T. Miyato, Robustness to adversarial perturbations in learning from incomplete data, J. Ngiam, D. Peng, V. Vasudevan, S. Kornblith, Q. V. Le, and R. Pang, Robustness properties of facebooks resnext wsl models, Adversarial dropout for supervised and semi-supervised learning, Lessons from building acoustic models with a million hours of speech, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), S. Qiao, W. Shen, Z. Zhang, B. Wang, and A. Yuille, Deep co-training for semi-supervised image recognition, I. Radosavovic, P. Dollr, R. Girshick, G. Gkioxari, and K. He, Data distillation: towards omni-supervised learning, A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, Semi-supervised learning with ladder networks, E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, Proceedings of the AAAI Conference on Artificial Intelligence, B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. 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. 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. We apply dropout to the final classification layer with a dropout rate of 0.5. 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. ImageNet . Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. Infer labels on a much larger unlabeled dataset. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. 2023.3.1_2 - 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]). 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. FixMatch-LS: Semi-supervised skin lesion classification with label Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. on ImageNet ReaL. . 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.
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