Here we study how to effectively use out-of-domain data. A common workaround is to use entropy minimization or ramp up the consistency loss. 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. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. For simplicity, we experiment with using 1128,164,132,116,14 of the whole data by uniformly sampling images from the the unlabeled set though taking the images with highest confidence leads to better results. Soft pseudo labels lead to better performance for low confidence data. The baseline model achieves an accuracy of 83.2. 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. Train a larger classifier on the combined set, adding noise (noisy student). These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. We will then show our results on ImageNet and compare them with state-of-the-art models. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. During the generation of the pseudo On, International journal of molecular sciences. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. However, manually annotating organs from CT scans is time . (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. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 27.8 to 16.1. et al. These CVPR 2020 papers are the Open Access versions, provided by the. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative We iterate this process by putting back the student as the teacher. This is probably because it is harder to overfit the large unlabeled dataset. 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. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Summarization_self-training_with_noisy_student_improves_imagenet_classification. self-mentoring outperforms data augmentation and self training. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. 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 accuracy is improved by about 10% in most settings. 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 Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. In the following, we will first describe experiment details to achieve our results. 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. 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. 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. Are you sure you want to create this branch? The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. to use Codespaces. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. Papers With Code is a free resource with all data licensed under. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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. We duplicate images in classes where there are not enough images. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet E. Arazo, D. Ortego, P. Albert, N. E. OConnor, and K. McGuinness, Pseudo-labeling and confirmation bias in deep semi-supervised learning, B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson, There are many consistent explanations of unlabeled data: why you should average, International Conference on Learning Representations, Advances in Neural Information Processing Systems, D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, MixMatch: a holistic approach to semi-supervised learning, Combining labeled and unlabeled data with co-training, C. Bucilu, R. Caruana, and A. Niculescu-Mizil, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Y. Carmon, A. Raghunathan, L. Schmidt, P. Liang, and J. C. Duchi, Unlabeled data improves adversarial robustness, Semi-supervised learning (chapelle, o. et al., eds. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. The performance drops when we further reduce it. 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. 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. , have shown that computer vision models lack robustness. We use the same architecture for the teacher and the student and do not perform iterative training. We iterate this process by We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Test images on ImageNet-P underwent different scales of perturbations. 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]. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. 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%. We present a simple self-training method that achieves 87.4 Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 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. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). For RandAugment, we apply two random operations with the magnitude set to 27. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical 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. We use a resolution of 800x800 in this experiment. As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Our procedure went as follows. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. We iterate this process by putting back the student as the teacher. Please refer to [24] for details about mFR and AlexNets flip probability. A novel random matrix theory based damping learner for second order optimisers inspired by linear shrinkage estimation is developed, and it is demonstrated that the derived method works well with adaptive gradient methods such as Adam. 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 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. Train a classifier on labeled data (teacher). Imaging, 39 (11) (2020), pp. (or is it just me), Smithsonian Privacy - : self-training_with_noisy_student_improves_imagenet_classification 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. Especially unlabeled images are plentiful and can be collected with ease. Train a larger classifier on the combined set, adding noise (noisy student). sign in The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Hence we use soft pseudo labels for our experiments unless otherwise specified. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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 then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Noisy StudentImageNetEfficientNet-L2state-of-the-art. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Code for Noisy Student Training. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). 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. 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. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . To intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct. A. Alemi, Thirty-First AAAI Conference on Artificial Intelligence, C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, EfficientNet: rethinking model scaling for convolutional neural networks, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, A. Vedaldi, M. Douze, and H. Jgou, Fixing the train-test resolution discrepancy, V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), J. Weston, F. Ratle, H. Mobahi, and R. Collobert, Deep learning via semi-supervised embedding, Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le, Unsupervised data augmentation for consistency training, S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, I. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. 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 . As can be seen from Table 8, the performance stays similar when we reduce the data to 116 of the total data, which amounts to 8.1M images after duplicating. 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. augmentation, dropout, stochastic depth to the student so that the noised 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. Parthasarathi et al. Le. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. on ImageNet, which is 1.0 Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. over the JFT dataset to predict a label for each image. This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. Finally, in the above, we say that the pseudo labels can be soft or hard. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. 10687-10698 Abstract As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Chowdhury et al. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. 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]. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. For classes where we have too many images, we take the images with the highest confidence. Noise Self-training with Noisy Student 1. 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. . 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. 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. The abundance of data on the internet is vast. 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. Their noise model is video specific and not relevant for image classification. We use EfficientNet-B0 as both the teacher model and the student model and compare using Noisy Student with soft pseudo labels and hard pseudo labels. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. 3.5B weakly labeled Instagram images. Use, Smithsonian 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. The abundance of data on the internet is vast. 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. It is expensive and must be done with great care. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. and surprising gains on robustness and adversarial benchmarks. 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. The algorithm is basically self-training, a method in semi-supervised learning (. 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. You signed in with another tab or window. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. Notice, Smithsonian Terms of Self-training with noisy student improves imagenet classification. Our work is based on self-training (e.g.,[59, 79, 56]). 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. 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]. Due to duplications, there are only 81M unique images among these 130M images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Please . Their purpose is different from ours: to adapt a teacher model on one domain to another. . This invariance constraint reduces the degrees of freedom in the model. 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. 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 . 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. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. ImageNet images and use it as a teacher to generate pseudo labels on 300M If nothing happens, download GitHub Desktop and try again. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. 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. unlabeled images. There was a problem preparing your codespace, please try again. Whether the model benefits from more unlabeled data depends on the capacity of the model since a small model can easily saturate, while a larger model can benefit from more data. It can be seen that masks are useful in improving classification performance. Their main goal is to find a small and fast model for deployment. We use the labeled images to train a teacher model using the standard cross entropy loss. First, we run an EfficientNet-B0 trained on ImageNet[69]. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher.