As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 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. In contrast, the predictions of the model with Noisy Student remain quite stable. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . We start with the 130M unlabeled images and gradually reduce the number of images. We duplicate images in classes where there are not enough images. 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. w Summary of key results compared to previous state-of-the-art models. Abdominal organ segmentation is very important for clinical applications. There was a problem preparing your codespace, please try again. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. IEEE Transactions on Pattern Analysis and Machine Intelligence. The mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. 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. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. IEEE Trans. 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]. 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. 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. 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 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. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. Classification of Socio-Political Event Data, SLADE: A Self-Training Framework For Distance Metric Learning, Self-Training with Differentiable Teacher, https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. Our study shows that using unlabeled data improves accuracy and general robustness. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. We iterate this process by While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. Self-training with Noisy Student improves ImageNet classification. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Train a classifier on labeled data (teacher). student is forced to learn harder from the pseudo labels. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Use Git or checkout with SVN using the web URL. To achieve this result, we first train an EfficientNet model on labeled However, in the case with 130M unlabeled images, with noise function removed, the performance is still improved to 84.3% from 84.0% when compared to the supervised baseline. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Imaging, 39 (11) (2020), pp. (using extra training data). Their noise model is video specific and not relevant for image classification. It is found that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. 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. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. 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]. 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. sign in The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. We use the labeled images to train a teacher model using the standard cross entropy loss. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. It 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 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]). Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Infer labels on a much larger unlabeled dataset. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. [^reference-9] [^reference-10] A critical insight was to . First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. We use stochastic depth[29], dropout[63] and RandAugment[14]. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Finally, in the above, we say that the pseudo labels can be soft or hard. Are labels required for improving adversarial robustness? Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet https://arxiv.org/abs/1911.04252. The results also confirm that vision models can benefit from Noisy Student even without iterative training. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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. The width. - : self-training_with_noisy_student_improves_imagenet_classification Le. 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. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. Work fast with our official CLI. 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 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. Cubuk, Improving robustness without sacrificing accuracy with patch gaussian augmentation, Y. Luo, J. Zhu, M. Li, Y. Ren, and B. Zhang, Smooth neighbors on teacher graphs for semi-supervised learning, L. Maale, C. K. Snderby, S. K. Snderby, and O. Winther, A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, Towards deep learning models resistant to adversarial attacks, D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten, Exploring the limits of weakly supervised pretraining, T. Miyato, S. Maeda, S. Ishii, and M. Koyama, Virtual adversarial training: a regularization method for supervised and semi-supervised learning, IEEE transactions on pattern analysis and machine intelligence, A. Najafi, S. Maeda, M. Koyama, and T. Miyato, Robustness to adversarial perturbations in learning from incomplete data, J. Ngiam, D. Peng, V. Vasudevan, S. Kornblith, Q. V. Le, and R. 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. ImageNet . The performance drops when we further reduce it. 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 putting back the student as the teacher. This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. It is expensive and must be done with great care. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. A common workaround is to use entropy minimization or ramp up the consistency loss. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Papers With Code is a free resource with all data licensed under. Please refer to [24] for details about mFR and AlexNets flip probability. Specifically, as all classes in ImageNet have a similar number of labeled images, we also need to balance the number of unlabeled images for each class. 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. 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. 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. It can be seen that masks are useful in improving classification performance. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. on ImageNet ReaL 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. It implements SemiSupervised Learning with Noise to create an Image Classification. and surprising gains on robustness and adversarial benchmarks. 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. 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. 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. We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. 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. Train a classifier on labeled data (teacher). unlabeled images. 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. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. 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. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. ImageNet images and use it as a teacher to generate pseudo labels on 300M But during the learning of the student, we inject noise such as data 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. 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. Semi-supervised medical image classification with relation-driven self-ensembling model. Edit social preview. 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. Self-Training Noisy Student " " Self-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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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. This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. labels, the teacher is not noised so that the pseudo labels are as good as Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. 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. sign in During this process, we kept increasing the size of the student model to improve the performance. Finally, frameworks in semi-supervised learning also include graph-based methods [84, 73, 77, 33], methods that make use of latent variables as target variables [32, 42, 78] and methods based on low-density separation[21, 58, 15], which might provide complementary benefits to our method. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Soft pseudo labels lead to better performance for low confidence data. For each class, we select at most 130K images that have the highest confidence. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. Learn more. Here we study how to effectively use out-of-domain data. unlabeled images , . Iterative training is not used here for simplicity. 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. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 10687-10698 Abstract Similar to[71], we fix the shallow layers during finetuning. 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. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Add a Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. In terms of methodology, We iterate this process by putting back the student as the teacher. Self-training with noisy student improves imagenet classification. et al. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Yalniz et al. 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. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . Self-training with Noisy Student improves ImageNet classification Abstract. 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. 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. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. The abundance of data on the internet is vast. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled 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.