The most interesting image is shown on the right of the first row. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. On, International journal of molecular sciences. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. We iterate this process by putting back the student as the teacher. 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. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin We use EfficientNet-B4 as both the teacher and the student. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. This material is presented to ensure timely dissemination of scholarly and technical work. The abundance of data on the internet is vast. Do better imagenet models transfer better? The baseline model achieves an accuracy of 83.2. Different kinds of noise, however, may have different effects. Different types of. 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. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. For more information about the large architectures, please refer to Table7 in Appendix A.1. [^reference-9] [^reference-10] A critical insight was to . Self-Training With Noisy Student Improves ImageNet Classification In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. In the following, we will first describe experiment details to achieve our results. Computer Science - Computer Vision and Pattern Recognition. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 In terms of methodology, et al. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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. If nothing happens, download GitHub Desktop and try again. Noisy Student (EfficientNet) - huggingface.co In this section, we study the importance of noise and the effect of several noise methods used in our model. We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled images. 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. Especially unlabeled images are plentiful and can be collected with ease. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. 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. Parthasarathi et al. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. 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 do not tune these hyperparameters extensively since our method is highly robust to them. We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. 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. Learn more. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Code is available at https://github.com/google-research/noisystudent. Self-training with noisy student improves imagenet classification. Train a larger classifier on the combined set, adding noise (noisy student). To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. Self-training with Noisy Student improves ImageNet classification. 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. 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]. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. possible. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. Self-Training with Noisy Student Improves ImageNet Classification 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Our experiments show that an important element for this simple method to work well at scale is that the student model should be noised during its training while the teacher should not be noised during the generation of pseudo labels. 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. On robustness test sets, it improves ImageNet-A top . Due to duplications, there are only 81M unique images among these 130M images. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Noisy Student (B7, L2) means to use EfficientNet-B7 as the student and use our best model with 87.4% accuracy as the teacher model. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 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. 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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. Self-Training With Noisy Student Improves ImageNet Classification 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. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Ranked #14 on In other words, small changes in the input image can cause large changes to the predictions. 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. 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. 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. . We apply dropout to the final classification layer with a dropout rate of 0.5. 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. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. If nothing happens, download Xcode and try again. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a The algorithm is basically self-training, a method in semi-supervised learning (. 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 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]). 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. ImageNet-A top-1 accuracy from 16.6 Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . By clicking accept or continuing to use the site, you agree to the terms outlined in our. Distillation Survey : Noisy Student | 9to5Tutorial Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. On . The performance consistently drops with noise function removed. 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. [57] used self-training for domain adaptation. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. Noisy StudentImageNetEfficientNet-L2state-of-the-art. ImageNet images and use it as a teacher to generate pseudo labels on 300M Why Self-training with Noisy Students beats SOTA Image classification GitHub - google-research/noisystudent: Code for Noisy Student Training 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. 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. 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. 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. Self-Training With Noisy Student Improves ImageNet Classification 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. Their noise model is video specific and not relevant for image classification. 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. . To intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. 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. 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. 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. 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 However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Chowdhury et al. 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. 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. . It implements SemiSupervised Learning with Noise to create an Image Classification. 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. . Train a classifier on labeled data (teacher). An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. As shown in Table2, Noisy Student with EfficientNet-L2 achieves 87.4% top-1 accuracy which is significantly better than the best previously reported accuracy on EfficientNet of 85.0%. 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. 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. In other words, the student is forced to mimic a more powerful ensemble model. 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. The main use case of knowledge distillation is model compression by making the student model smaller. 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. We determine number of training steps and the learning rate schedule by the batch size for labeled images. 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. sign in A semi-supervised segmentation network based on noisy student learning putting back the student as the 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]. 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 shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 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. 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. 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. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Work fast with our official CLI. Self-training with Noisy Student improves ImageNet classification 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. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative 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, qvlg@google.com, hovy@cmu.edu Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . These significant gains in robustness in ImageNet-C and ImageNet-P are surprising because our models were not deliberately optimizing for robustness (e.g., via data augmentation). Use Git or checkout with SVN using the web URL. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 2023.3.1_2 - We iterate this process by putting back the student as the teacher. As can be seen from Table 8, the performance stays similar when we reduce the data to 116 of the total data, which amounts to 8.1M images after duplicating. on ImageNet, which is 1.0 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). Agreement NNX16AC86A, Is ADS down? Self-training with Noisy Student improves ImageNet classification We start with the 130M unlabeled images and gradually reduce the number of images. 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 will then show our results on ImageNet and compare them with state-of-the-art models. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). However, manually annotating organs from CT scans is time . We also list EfficientNet-B7 as a reference. Self-Training With Noisy Student Improves ImageNet Classification If nothing happens, download GitHub Desktop and try again. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. We use a resolution of 800x800 in this experiment. (using extra training data).
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