Work fast with our official CLI. 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. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. Please Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Work fast with our official CLI. 3.5B weakly labeled Instagram images. 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 . sign in The baseline model achieves an accuracy of 83.2. 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. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. We present a simple self-training method that achieves 87.4 Soft pseudo labels lead to better performance for low confidence data. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. The performance drops when we further reduce it. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. labels, the teacher is not noised so that the pseudo labels are as good as We thank the Google Brain team, Zihang Dai, Jeff Dean, Hieu Pham, Colin Raffel, Ilya Sutskever and Mingxing Tan for insightful discussions, Cihang Xie for robustness evaluation, Guokun Lai, Jiquan Ngiam, Jiateng Xie and Adams Wei Yu for feedbacks on the draft, Yanping Huang and Sameer Kumar for improving TPU implementation, Ekin Dogus Cubuk and Barret Zoph for help with RandAugment, Yanan Bao, Zheyun Feng and Daiyi Peng for help with the JFT dataset, Olga Wichrowska and Ola Spyra for help with infrastructure. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. Astrophysical Observatory. 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. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. 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. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. On robustness test sets, it improves 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. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). We apply dropout to the final classification layer with a dropout rate of 0.5. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. 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. 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. We then use the teacher model to generate pseudo labels on unlabeled images. Noisy Student Training is a semi-supervised learning approach. We then select images that have confidence of the label higher than 0.3. The abundance of data on the internet is vast. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Train a larger classifier on the combined set, adding noise (noisy student). The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). to use Codespaces. In contrast, the predictions of the model with Noisy Student remain quite stable. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. Their purpose is different from ours: to adapt a teacher model on one domain to another. Yalniz et al. This is probably because it is harder to overfit the large unlabeled dataset. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical You signed in with another tab or window. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. 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. Noisy Student can still improve the accuracy to 1.6%. on ImageNet, which is 1.0 By clicking accept or continuing to use the site, you agree to the terms outlined in our. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. combination of labeled and pseudo labeled images. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). 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. 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. 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. Papers With Code is a free resource with all data licensed under. Med. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. on ImageNet ReaL 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. 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. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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]. . In the following, we will first describe experiment details to achieve our results. For more information about the large architectures, please refer to Table7 in Appendix A.1. To achieve this result, we first train an EfficientNet model on labeled 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. Are you sure you want to create this branch? student is forced to learn harder from the pseudo labels. The main difference between our method and knowledge distillation is that knowledge distillation does not consider unlabeled data and does not aim to improve the student 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. 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. 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. Edit social preview. 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. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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 all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Ranked #14 on 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. We iterate this process by putting back the student as the teacher. 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. 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. 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. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Computer Science - Computer Vision and Pattern Recognition. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: For ImageNet checkpoints trained by Noisy Student Training, please refer to the EfficientNet github. 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. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. For each class, we select at most 130K images that have the highest confidence. If nothing happens, download GitHub Desktop and try again. 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. Do imagenet classifiers generalize to imagenet? (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. [^reference-9] [^reference-10] A critical insight was to . The algorithm is basically self-training, a method in semi-supervised learning (. 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. We sample 1.3M images in confidence intervals. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled 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. 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. These CVPR 2020 papers are the Open Access versions, provided by the. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. 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. 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. The results also confirm that vision models can benefit from Noisy Student even without iterative training. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. On, International journal of molecular sciences. . 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. 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.