self training with noisy student improves imagenet classification

We use the same architecture for the teacher and the student and do not perform iterative training. 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. 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 way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. 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. The learning rate starts at 0.128 for labeled batch size 2048 and decays by 0.97 every 2.4 epochs if trained for 350 epochs or every 4.8 epochs if trained for 700 epochs. Code for Noisy Student Training. 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. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled 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 . We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. 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. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical possible. combination of labeled and pseudo labeled images. (Submitted on 11 Nov 2019) We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. 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. 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. The results also confirm that vision models can benefit from Noisy Student even without iterative training. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 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. In particular, we first perform normal training with a smaller resolution for 350 epochs. 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. If nothing happens, download Xcode and try again. For more information about the large architectures, please refer to Table7 in Appendix A.1. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. First, a teacher model is trained in a supervised fashion. 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. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. The abundance of data on the internet is vast. Please refer to [24] for details about mCE and AlexNets error rate. 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. 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 found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. We use stochastic depth[29], dropout[63] and RandAugment[14]. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. sign in mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We then perform data filtering and balancing on this corpus. 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. 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. 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. 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. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 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. 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]. A tag already exists with the provided branch name. 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. student is forced to learn harder from the pseudo labels. We then train a larger EfficientNet as a student model on the 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. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . You signed in with another tab or window. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. We sample 1.3M images in confidence intervals. 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. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Add a 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. supervised model from 97.9% accuracy to 98.6% accuracy. During this process, we kept increasing the size of the student model to improve the performance. 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. This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. 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. . Zoph et al. Noisy Student leads to significant improvements across all model sizes for EfficientNet. 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 abundance of data on the internet is vast. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. In other words, small changes in the input image can cause large changes to the predictions. We then use the teacher model to generate pseudo labels on unlabeled images. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10687-10698, (2020 . We then select images that have confidence of the label higher than 0.3. Use Git or checkout with SVN using the web URL. 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. You signed in with another tab or window. To achieve this result, we first train an EfficientNet model on labeled Le. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Their purpose is different from ours: to adapt a teacher model on one domain to another. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Computer Science - Computer Vision and Pattern Recognition. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . This invariance constraint reduces the degrees of freedom in the model. We use EfficientNet-B4 as both the teacher and the student. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. putting back the student as the teacher. 10687-10698). Compared to consistency training[45, 5, 74], the self-training / teacher-student framework is better suited for ImageNet because we can train a good teacher on ImageNet using label data. We improved it by adding noise to the student to learn beyond the teachers knowledge. On robustness test sets, it improves ImageNet-A top . on ImageNet ReaL It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images The main use case of knowledge distillation is model compression by making the student model smaller. 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 determine number of training steps and the learning rate schedule by the batch size for labeled images. Noisy StudentImageNetEfficientNet-L2state-of-the-art. (or is it just me), Smithsonian Privacy Copyright and all rights therein are retained by authors or by other copyright holders. We also list EfficientNet-B7 as a reference. 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]. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. 10687-10698 Abstract We iterate this process by putting back the student as the teacher. 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. 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. unlabeled images. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Here we study if it is possible to improve performance on small models by using a larger teacher model, since small models are useful when there are constraints for model size and latency in real-world applications. Our study shows that using unlabeled data improves accuracy and general robustness. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model task. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Code for Noisy Student Training. But during the learning of the student, we inject noise such as data 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. 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 inputs to the algorithm are both labeled and unlabeled images. The algorithm is basically self-training, a method in semi-supervised learning (. By clicking accept or continuing to use the site, you agree to the terms outlined in our. The architectures for the student and teacher models can be the same or different. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. https://arxiv.org/abs/1911.04252. 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 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 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. A common workaround is to use entropy minimization or ramp up the consistency loss. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. , have shown that computer vision models lack robustness. In other words, the student is forced to mimic a more powerful ensemble model. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Their noise model is video specific and not relevant for image classification. 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. w Summary of key results compared to previous state-of-the-art models. 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 you get a better model, you can use the model to predict pseudo-labels on the filtered data. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (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. 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. 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. It implements SemiSupervised Learning with Noise to create an Image Classification. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. 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. Please refer to [24] for details about mFR and AlexNets flip probability. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 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. We will then show our results on ImageNet and compare them with state-of-the-art models. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. sign in Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Code is available at https://github.com/google-research/noisystudent. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Work fast with our official CLI. Learn more. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. There was a problem preparing your codespace, please try again. 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. For classes where we have too many images, we take the images with the highest confidence. Yalniz et al. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. On robustness test sets, it improves Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 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. Please 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. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. But training robust supervised learning models is requires this step. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Different kinds of noise, however, may have different effects. Do imagenet classifiers generalize to imagenet? Agreement NNX16AC86A, Is ADS down? 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. and surprising gains on robustness and adversarial benchmarks. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. Further, Noisy Student outperforms the state-of-the-art accuracy of 86.4% by FixRes ResNeXt-101 WSL[44, 71] that requires 3.5 Billion Instagram images labeled with tags. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. ImageNet images and use it as a teacher to generate pseudo labels on 300M Our main results are shown in Table1. 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. 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. We start with the 130M unlabeled images and gradually reduce the number of images. Hence we use soft pseudo labels for our experiments unless otherwise specified. . 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. 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. 27.8 to 16.1. The main difference between Data Distillation and our method is that we use the noise to weaken the student, which is the opposite of their approach of strengthening the teacher by ensembling. Please Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality 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. 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.