A common workaround is to use entropy minimization or ramp up the consistency loss. https://arxiv.org/abs/1911.04252. Use Git or checkout with SVN using the web URL. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. To achieve strong results on ImageNet, the student model also needs to be large, typically larger than common vision models, so that it can leverage a large number of unlabeled images. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . 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. Code is available at https://github.com/google-research/noisystudent. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. 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. The architectures for the student and teacher models can be the same or different. We sample 1.3M images in confidence intervals. 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. There was a problem preparing your codespace, please try again. Med. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. . 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. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). For classes where we have too many images, we take the images with the highest confidence. on ImageNet ReaL Astrophysical Observatory. 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 then train a larger EfficientNet as a student model on the 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. Parthasarathi et al. 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. We use the same architecture for the teacher and the student and do not perform iterative training. 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. Code for Noisy Student Training. 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. Noisy Students performance improves with more unlabeled data. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. Noise Self-training with Noisy Student 1. 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 present a simple self-training method that achieves 87.4 Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from . During this process, we kept increasing the size of the student model to improve the performance. 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 . 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]. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Summarization_self-training_with_noisy_student_improves_imagenet_classification. 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. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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. 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. 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Self-training with Noisy Student improves ImageNet classification. For RandAugment, we apply two random operations with the magnitude set to 27. Use Git or checkout with SVN using the web URL. ImageNet-A top-1 accuracy from 16.6 Then, that teacher is used to label the unlabeled data. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. 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. On, International journal of molecular sciences. 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 iterate this process by C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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. all 12, Image Classification We will then show our results on ImageNet and compare them with state-of-the-art models. 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. sign in 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. In this section, we study the importance of noise and the effect of several noise methods used in our model. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. augmentation, dropout, stochastic depth to the student so that the noised The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. We also study the effects of using different amounts of unlabeled data. These CVPR 2020 papers are the Open Access versions, provided by the. Copyright and all rights therein are retained by authors or by other copyright holders. Are you sure you want to create this branch? We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. On robustness test sets, it improves ImageNet-A top . 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 find that Noisy Student is better with an additional trick: data balancing. In the following, we will first describe experiment details to achieve our results. If nothing happens, download GitHub Desktop and try again. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. 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. We iterate this process by putting back the student as the teacher. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Work fast with our official CLI. 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. 3.5B weakly labeled Instagram images. 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. A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. and surprising gains on robustness and adversarial benchmarks. 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 During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Learn more. 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. 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. Different kinds of noise, however, may have different effects. It is expensive and must be done with great care. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. 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. 27.8 to 16.1. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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 find that using a batch size of 512, 1024, and 2048 leads to the same performance. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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. 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. Yalniz et al. Work fast with our official CLI. 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. 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. 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. Flip probability is the probability that the model changes top-1 prediction for different perturbations. 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. Our procedure went as follows. https://arxiv.org/abs/1911.04252, Accompanying notebook and sources to "A Guide to Pseudolabelling: How to get a Kaggle medal with only one model" (Dec. 2020 PyData Boston-Cambridge Keynote), Deep learning has shown remarkable successes in image recognition in recent years[35, 66, 62, 23, 69]. 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. Noisy Student can still improve the accuracy to 1.6%. Ranked #14 on On robustness test sets, it improves In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. Self-training 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. For more information about the large architectures, please refer to Table7 in Appendix A.1. To date (2020) we will introduce "Noisy Student Training", which is a state-of-the-art model.The idea is to extend self-training and Distillation, a paper that shows that by adding three noises and distilling multiple times, the student model will have better generalization performance than the teacher model. If nothing happens, download GitHub Desktop and try again. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 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. 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. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. 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. The abundance of data on the internet is vast. 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. . In particular, we first perform normal training with a smaller resolution for 350 epochs. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Agreement NNX16AC86A, Is ADS down? 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. Self-training with Noisy Student improves ImageNet classification. [^reference-9] [^reference-10] A critical insight was to . Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical We start with the 130M unlabeled images and gradually reduce the number of images. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative 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. 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). For each class, we select at most 130K images that have the highest confidence. 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. Please Are you sure you want to create this branch? Their noise model is video specific and not relevant for image classification. (or is it just me), Smithsonian Privacy Imaging, 39 (11) (2020), pp. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Infer labels on a much larger unlabeled dataset. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . , have shown that computer vision models lack robustness. et al. This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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]. This material is presented to ensure timely dissemination of scholarly and technical work. Train a larger classifier on the combined set, adding noise (noisy student). We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Train a classifier on labeled data (teacher). Due to duplications, there are only 81M unique images among these 130M images. Hence the total number of images that we use for training a student model is 130M (with some duplicated images).