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There was a problem preparing your codespace, please try again. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model On . As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. Infer labels on a much larger unlabeled dataset. on ImageNet ReaL The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Le. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). The main use case of knowledge distillation is model compression by making the student model smaller. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. 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. Flip probability is the probability that the model changes top-1 prediction for different perturbations. 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 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]. Are you sure you want to create this branch? ; 2006)[book reviews], Semi-supervised deep learning with memory, Proceedings of the European Conference on Computer Vision (ECCV), Xception: deep learning with depthwise separable convolutions, K. Clark, M. Luong, C. D. Manning, and Q. V. Le, Semi-supervised sequence modeling with cross-view training, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, AutoAugment: learning augmentation strategies from data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, RandAugment: practical data augmentation with no separate search, Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, Good semi-supervised learning that requires a bad gan, T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, A. Galloway, A. Golubeva, T. Tanay, M. Moussa, and G. W. Taylor, R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow, I. J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and harnessing adversarial examples, Semi-supervised learning by entropy minimization, Advances in neural information processing systems, K. Gu, B. Yang, J. Ngiam, Q. 3429-3440. . In this section, we study the importance of noise and the effect of several noise methods used in our model. 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. In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. Our work is based on self-training (e.g.,[59, 79, 56]). Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . 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. 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. Noisy Students performance improves with more unlabeled data. During the generation of the pseudo Especially unlabeled images are plentiful and can be collected with ease. 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. 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]. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. It is expensive and must be done with great care. Self-Training With Noisy Student Improves ImageNet Classification. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Work fast with our official CLI. However, manually annotating organs from CT scans is time . Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. Infer labels on a much larger unlabeled dataset. We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. 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. At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. The performance consistently drops with noise function removed. With Noisy Student, the model correctly predicts dragonfly for the image. [57] used self-training for domain adaptation. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. 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. 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. Use Git or checkout with SVN using the web URL. Noisy StudentImageNetEfficientNet-L2state-of-the-art. The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. We iterate this process by putting back the student as the teacher. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. First, we run an EfficientNet-B0 trained on ImageNet[69]. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. 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. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. augmentation, dropout, stochastic depth to the student so that the noised This material is presented to ensure timely dissemination of scholarly and technical work. Finally, in the above, we say that the pseudo labels can be soft or hard. 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. We use the same architecture for the teacher and the student and do not perform iterative training. 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. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 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. We iterate this process by putting back the student as the teacher. 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. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. You signed in with another tab or window. Ranked #14 on We will then show our results on ImageNet and compare them with state-of-the-art models. 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 evaluate the best model, that achieves 87.4% top-1 accuracy, on three robustness test sets: ImageNet-A, ImageNet-C and ImageNet-P. ImageNet-C and P test sets[24] include images with common corruptions and perturbations such as blurring, fogging, rotation and scaling. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Please refer to [24] for details about mFR and AlexNets flip probability. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Self-training 1 2Self-training 3 4n What is Noisy Student? w Summary of key results compared to previous state-of-the-art models. (using extra training data). But during the learning of the student, we inject noise such as data Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. . 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 find that Noisy Student is better with an additional trick: data balancing. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. et al. 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. 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. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. sign in We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. This model investigates a new method. Edit social preview. Notice, Smithsonian Terms of Agreement NNX16AC86A, Is ADS down? In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. Train a larger classifier on the combined set, adding noise (noisy student). We use the standard augmentation instead of RandAugment in this experiment. 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. These CVPR 2020 papers are the Open Access versions, provided by the. We also list EfficientNet-B7 as a reference. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. to use Codespaces. We then select images that have confidence of the label higher than 0.3. 3.5B weakly labeled Instagram images. 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. The algorithm is basically self-training, a method in semi-supervised learning (. Are you sure you want to create this branch? Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le Description: 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. over the JFT dataset to predict a label for each image. Especially unlabeled images are plentiful and can be collected with ease. 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. The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. This is probably because it is harder to overfit the large unlabeled dataset. , have shown that computer vision models lack robustness. The main difference between our work and prior works is that we identify the importance of noise, and aggressively inject noise to make the student better. This invariance constraint reduces the degrees of freedom in the model. We iterate this process by putting back the student as the teacher. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes.