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. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . We first report the validation set accuracy on the ImageNet 2012 ILSVRC challenge prediction task as commonly done in literature[35, 66, 23, 69] (see also [55]). Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. [57] used self-training for domain adaptation. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. The proposed use of distillation to only handle easy instances allows for a more aggressive trade-off in the student size, thereby reducing the amortized cost of inference and achieving better accuracy than standard distillation. Abdominal organ segmentation is very important for clinical applications. 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. 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 model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. We do not tune these hyperparameters extensively since our method is highly robust to them. Copyright and all rights therein are retained by authors or by other copyright holders. 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]. 10687-10698). On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. 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. If nothing happens, download Xcode and try again. 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. Le. 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 . Are labels required for improving adversarial robustness? 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Papers With Code is a free resource with all data licensed under. When data augmentation noise is used, the student must ensure that a translated image, for example, should have the same category with a non-translated image. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Use Git or checkout with SVN using the web URL. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. Iterative training is not used here for simplicity. We also list EfficientNet-B7 as a reference. 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. We start with the 130M unlabeled images and gradually reduce the number of images. These CVPR 2020 papers are the Open Access versions, provided by the. 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. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . 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. supervised model from 97.9% accuracy to 98.6% accuracy. Self-Training Noisy Student " " Self-Training . 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. 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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. 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. By showing the models only labeled images, we limit ourselves from making use of unlabeled images available in much larger quantities to improve accuracy and robustness of state-of-the-art models. 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. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. We use stochastic depth[29], dropout[63] and RandAugment[14]. The accuracy is improved by about 10% in most settings. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 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. 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. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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 . 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. Please refer to [24] for details about mFR and AlexNets flip probability. Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Edit social preview. 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. Parthasarathi et al. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Classification of Socio-Political Event Data, SLADE: A Self-Training Framework For Distance Metric Learning, Self-Training with Differentiable Teacher, https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. Our study shows that using unlabeled data improves accuracy and general robustness. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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]. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). 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. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. IEEE Transactions on Pattern Analysis and Machine Intelligence. It can be seen that masks are useful in improving classification performance. . to use Codespaces. 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. 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. 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. Different types of. We iterate this process by putting back the student as the teacher. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. w Summary of key results compared to previous state-of-the-art models. . Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen, GPipe: efficient training of giant neural networks using pipeline parallelism, A. Iscen, G. Tolias, Y. Avrithis, and O. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. We apply dropout to the final classification layer with a dropout rate of 0.5. 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. 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. . 27.8 to 16.1. During this process, we kept increasing the size of the student model to improve the performance. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. If nothing happens, download Xcode and try again. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Flip probability is the probability that the model changes top-1 prediction for different perturbations. ImageNet . However, manually annotating organs from CT scans is time . 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. 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. 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. Our procedure went as follows. There was a problem preparing your codespace, please try again. 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. We also study the effects of using different amounts of unlabeled data. Noisy Student Training seeks to improve on self-training and distillation in two ways. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. Yalniz et al. 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. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. 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). 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. sign in A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet. In the following, we will first describe experiment details to achieve our results. 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. 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. Learn more. Especially unlabeled images are plentiful and can be collected with ease. 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. 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 EfficientNet with Noisy Student produces correct top-1 predictions (shown in. At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. On robustness test sets, it improves 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. The architectures for the student and teacher models can be the same or different. Work fast with our official CLI. 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. 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. Self-training with Noisy Student improves ImageNet classification. In typical self-training with the teacher-student framework, noise injection to the student is not used by default, or the role of noise is not fully understood or justified. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. We use a resolution of 800x800 in this experiment. Summarization_self-training_with_noisy_student_improves_imagenet_classification. This material is presented to ensure timely dissemination of scholarly and technical work. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. We determine number of training steps and the learning rate schedule by the batch size for labeled images. We will then show our results on ImageNet and compare them with state-of-the-art models. . "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. See combination of labeled and pseudo labeled images. Code is available at https://github.com/google-research/noisystudent. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. 3.5B weakly labeled Instagram images. Works based on pseudo label[37, 31, 60, 1] are similar to self-training, but also suffers the same problem with consistency training, since it relies on a model being trained instead of a converged model with high accuracy to generate pseudo labels. After using the masks generated by teacher-SN, the classification performance improved by 0.2 of AC, 1.2 of SP, and 0.7 of AUC. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy.

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