Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. It is expensive and must be done with great care. You signed in with another tab or window. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. We present a simple self-training method that achieves 87.4 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 . Self-training with Noisy Student improves ImageNet classification Noisy StudentImageNetEfficientNet-L2state-of-the-art. A number of studies, e.g. First, a teacher model is trained in a supervised fashion. Self-Training With Noisy Student Improves ImageNet Classification 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. on ImageNet ReaL This is probably because it is harder to overfit the large unlabeled dataset. Self-training with Noisy Student improves ImageNet classification Abstract. Self-training with Noisy Student - Medium We iterate this process by putting back the student as the teacher. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Specifically, we train the student model for 350 epochs for models larger than EfficientNet-B4, including EfficientNet-L0, L1 and L2 and train the student model for 700 epochs for smaller models. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. 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. [57] used self-training for domain adaptation. Self-training 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. putting back the student as the teacher. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. 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. Work fast with our official CLI. PDF Self-Training with Noisy Student Improves ImageNet Classification 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. - : self-training_with_noisy_student_improves_imagenet_classification Work fast with our official CLI. . 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. Are labels required for improving adversarial robustness? 3.5B weakly labeled Instagram images. This invariance constraint reduces the degrees of freedom in the model. Zoph et al. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. 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. We use the standard augmentation instead of RandAugment in this experiment. Self-Training with Noisy Student Improves ImageNet Classification In particular, we first perform normal training with a smaller resolution for 350 epochs. Yalniz et al. 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Notice, Smithsonian Terms of Noisy Student Explained | Papers With Code In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Please A tag already exists with the provided branch name. [68, 24, 55, 22]. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. et al. 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. CLIP: Connecting text and images - OpenAI We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. 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. 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 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]. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. On robustness test sets, it improves ImageNet-A top . Here we study how to effectively use out-of-domain data. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. IEEE Transactions on Pattern Analysis and Machine Intelligence. Hence we use soft pseudo labels for our experiments unless otherwise specified. 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. unlabeled 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. to use Codespaces. 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. Models are available at this https URL. Noisy Student Training is a semi-supervised learning approach. Our procedure went as follows. Papers With Code is a free resource with all data licensed under. Please refer to [24] for details about mCE and AlexNets error rate. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 2023.3.1_2 - Image Classification Self-training with Noisy Student - But training robust supervised learning models is requires this step. We improved it by adding noise to the student to learn beyond the teachers knowledge. 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. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Self-Training : Noisy Student : 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 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. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. . On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . 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. This model investigates a new method. 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. If nothing happens, download Xcode and try again. 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. 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. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Distillation Survey : Noisy Student | 9to5Tutorial ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. Code for Noisy Student Training. w Summary of key results compared to previous state-of-the-art models. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 10687-10698 Abstract [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . The abundance of data on the internet is vast. 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]. Use Git or checkout with SVN using the web URL. (using extra training data). GitHub - google-research/noisystudent: Code for Noisy Student Training Self-training was previously used to improve ResNet-50 from 76.4% to 81.2% top-1 accuracy[76] which is still far from the state-of-the-art accuracy. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. A semi-supervised segmentation network based on noisy student learning 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. Please (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. Self-training with Noisy Student improves ImageNet classification 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. , have shown that computer vision models lack robustness. As shown in Table2, Noisy Student with EfficientNet-L2 achieves 87.4% top-1 accuracy which is significantly better than the best previously reported accuracy on EfficientNet of 85.0%. 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. 10687-10698). Self-training with Noisy Student improves ImageNet classification tsai - Noisy student Summarization_self-training_with_noisy_student_improves_imagenet [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. In terms of methodology, If nothing happens, download Xcode and try again. Our study shows that using unlabeled data improves accuracy and general robustness. 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. 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 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. ImageNet-A top-1 accuracy from 16.6 The architectures for the student and teacher models can be the same or different. If nothing happens, download GitHub Desktop and try again. and surprising gains on robustness and adversarial benchmarks. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. First, we run an EfficientNet-B0 trained on ImageNet[69]. The results are shown in Figure 4 with the following observations: (1) Soft pseudo labels and hard pseudo labels can both lead to great improvements with in-domain unlabeled images i.e., high-confidence images. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. over the JFT dataset to predict a label for each image. 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]. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. We will then show our results on ImageNet and compare them with state-of-the-art models. all 12, Image 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. [^reference-9] [^reference-10] A critical insight was to . Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. You signed in with another tab or window. 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. However an important requirement for Noisy Student to work well is that the student model needs to be sufficiently large to fit more data (labeled and pseudo labeled). In other words, the student is forced to mimic a more powerful ensemble model. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines.
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