However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. However, manually annotating organs from CT scans is time . (using extra training data). As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Are you sure you want to create this branch? mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. 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. 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. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. 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. Especially unlabeled images are plentiful and can be collected with ease. Summarization_self-training_with_noisy_student_improves_imagenet_classification. This invariance constraint reduces the degrees of freedom in the model. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). w Summary of key results compared to previous state-of-the-art models. 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. Here we use unlabeled images to improve the state-of-the-art ImageNet accuracy and show that the accuracy gain has an outsized impact on robustness. 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. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images student is forced to learn harder from the pseudo labels. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Do better imagenet models transfer better? During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. First, a teacher model is trained in a supervised fashion. When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single model. The algorithm is basically self-training, a method in semi-supervised learning (. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. We do not tune these hyperparameters extensively since our method is highly robust to them. If nothing happens, download Xcode and try again. Notice, Smithsonian Terms of "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. labels, the teacher is not noised so that the pseudo labels are as good as We also list EfficientNet-B7 as a reference. Le. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. The most interesting image is shown on the right of the first row. This is probably because it is harder to overfit the large unlabeled dataset. 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. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. Are you sure you want to create this branch? Use Git or checkout with SVN using the web URL. ImageNet-A top-1 accuracy from 16.6 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. Finally, we iterate the algorithm a few times by treating the student as a teacher to generate new pseudo labels and train a new student. possible. We use a resolution of 800x800 in this experiment. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. Models are available at this https URL. We used the version from [47], which filtered the validation set of ImageNet. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a Ranked #14 on [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Image Classification Their noise model is video specific and not relevant for image classification. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. 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. If nothing happens, download Xcode and try again. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. This model investigates a new method. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. Code is available at https://github.com/google-research/noisystudent. 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. 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. 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. 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. team using this approach not only surpasses the top-1 ImageNet accuracy of SOTA models by 1%, it also shows that the robustness of a model also improves. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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 with Noisy Student improves ImageNet classification. Code for Noisy Student Training. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. 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. 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. In other words, the student is forced to mimic a more powerful ensemble model. self-mentoring outperforms data augmentation and self training. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. But during the learning of the student, we inject noise such as data On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. 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. supervised model from 97.9% accuracy to 98.6% accuracy. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Hence we use soft pseudo labels for our experiments unless otherwise specified. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. 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. We iterate this process by putting back the student as the teacher. Works based on pseudo label[37, 31, 60, 1] are similar to self-training, but also suffers the same problem with consistency training, since it relies on a model being trained instead of a converged model with high accuracy to generate pseudo labels. We then train a larger EfficientNet as a student model on the . Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. These CVPR 2020 papers are the Open Access versions, provided by the. Noisy Student can still improve the accuracy to 1.6%. In the following, we will first describe experiment details to achieve our results. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In particular, we first perform normal training with a smaller resolution for 350 epochs. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. The architectures for the student and teacher models can be the same or different. For each class, we select at most 130K images that have the highest confidence. 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. Our procedure went as follows. Infer labels on a much larger unlabeled dataset. 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 duplicate images in classes where there are not enough images. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. 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. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . Use Git or checkout with SVN using the web URL. 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. We then use the teacher model to generate pseudo labels on unlabeled images. If nothing happens, download GitHub Desktop and try again. , have shown that computer vision models lack robustness. 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. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. 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. Self-training with Noisy Student improves ImageNet classification Abstract. Edit social preview. 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. During this process, we kept increasing the size of the student model to improve the performance. It can be seen that masks are useful in improving classification performance. 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. . 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. Abdominal organ segmentation is very important for clinical applications. 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. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models.
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