student is forced to learn harder from the pseudo labels. Self-training with Noisy Student improves ImageNet classification. Work fast with our official CLI. Learn more. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The most interesting image is shown on the right of the first row. 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. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 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. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. In contrast, the predictions of the model with Noisy Student remain quite stable. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. Learn more. Train a classifier on labeled data (teacher). This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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. Infer labels on a much larger unlabeled dataset. 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. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. [68, 24, 55, 22]. Self-Training With Noisy Student Improves ImageNet Classification @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019 . The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative However, manually annotating organs from CT scans is time . Summarization_self-training_with_noisy_student_improves_imagenet . 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. 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. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Med. The algorithm is basically self-training, a method in semi-supervised learning (. There was a problem preparing your codespace, please try again. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Imaging, 39 (11) (2020), pp. 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. 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. We then perform data filtering and balancing on this corpus. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Their noise model is video specific and not relevant for image classification. We duplicate images in classes where there are not enough images. 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]. 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. Self-training with Noisy Student - Medium 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model 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 But training robust supervised learning models is requires this step. Their purpose is different from ours: to adapt a teacher model on one domain to another. Self-training with Noisy Student improves ImageNet classification 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. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Self-training with Noisy Student improves ImageNet classification 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. 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. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin 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. (using extra training data). A number of studies, e.g. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. Especially unlabeled images are plentiful and can be collected with ease. 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. This is probably because it is harder to overfit the large unlabeled dataset. 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. The performance drops when we further reduce it. 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. Soft pseudo labels lead to better performance for low confidence data. Are you sure you want to create this branch? Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. 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. First, a teacher model is trained in a supervised fashion. 10687-10698). FixMatch-LS: Semi-supervised skin lesion classification with label 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]. Noisy Student self-training is an effective way to leverage unlabelled datasets and improving accuracy by adding noise to the student model while training so it learns beyond the teacher's knowledge. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. CVPR 2020 Open Access Repository 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. Abdominal organ segmentation is very important for clinical applications. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). on ImageNet, which is 1.0 Distillation Survey : Noisy Student | 9to5Tutorial The main use case of knowledge distillation is model compression by making the student model smaller. 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. sign in 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. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. Self-training with Noisy Student improves ImageNet classification [57] used self-training for domain adaptation. 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. 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. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. unlabeled images , . The main difference between our work and prior works is that we identify the importance of noise, and aggressively inject noise to make the student better. Self-training 1 2Self-training 3 4n What is Noisy Student? The abundance of data on the internet is vast. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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]. task. Their main goal is to find a small and fast model for deployment. combination of labeled and pseudo labeled images. Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. 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. Especially unlabeled images are plentiful and can be collected with ease. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. 2023.3.1_2 - This invariance constraint reduces the degrees of freedom in the model. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. Self-training with Noisy Student improves ImageNet classification Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 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 contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Different kinds of noise, however, may have different effects. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. We then select images that have confidence of the label higher than 0.3. 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. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Train a larger classifier on the combined set, adding noise (noisy student). 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. Noisy Student leads to significant improvements across all model sizes for EfficientNet. Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y. 3.5B weakly labeled Instagram images. We iterate this process by putting back the student as the teacher. 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. 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. Work fast with our official CLI. There was a problem preparing your codespace, please try again. We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled images. 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. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . https://arxiv.org/abs/1911.04252. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. It is expensive and must be done with great care. A common workaround is to use entropy minimization or ramp up the consistency loss. Use Git or checkout with SVN using the web URL. On robustness test sets, it improves ImageNet-A top . The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Self-Training With Noisy Student Improves ImageNet Classification The inputs to the algorithm are both labeled and unlabeled images. Flip probability is the probability that the model changes top-1 prediction for different perturbations. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Notice, Smithsonian Terms of GitHub - google-research/noisystudent: Code for Noisy Student 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. 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. The width. Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. Noisy Student Training seeks to improve on self-training and distillation in two ways. 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. Self-Training for Natural Language Understanding! These significant gains in robustness in ImageNet-C and ImageNet-P are surprising because our models were not deliberately optimizing for robustness (e.g., via data augmentation). Different types of. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. We iterate this process by For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. - : self-training_with_noisy_student_improves_imagenet_classification 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. IEEE Trans. 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. We will then show our results on ImageNet and compare them with state-of-the-art models. 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 Le. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We use the same architecture for the teacher and the student and do not perform iterative training. 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. We apply dropout to the final classification layer with a dropout rate of 0.5. Copyright and all rights therein are retained by authors or by other copyright holders. Our procedure went as follows. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. 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. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Please 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 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. . 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. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . Code is available at https://github.com/google-research/noisystudent. A tag already exists with the provided branch name. 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.
