However, manually annotating organs from CT scans is time . In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. 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.
PDF Self-Training with Noisy Student Improves ImageNet Classification Train a larger classifier on the combined set, adding noise (noisy student). It can be seen that masks are useful in improving classification performance. But during the learning of the student, we inject noise such as data These CVPR 2020 papers are the Open Access versions, provided by the. Due to duplications, there are only 81M unique images among these 130M images. 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. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. The accuracy is improved by about 10% in most settings.
Self-Training With Noisy Student Improves ImageNet Classification 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. The abundance of data on the internet is vast. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet.
In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Especially unlabeled images are plentiful and can be collected with ease. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . 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. 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. . 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. 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. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3.5B weakly labeled Instagram images. to use Codespaces. We determine number of training steps and the learning rate schedule by the batch size for labeled 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. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. . 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. 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. Imaging, 39 (11) (2020), pp. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. 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. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 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.
GitHub - google-research/noisystudent: Code for Noisy Student Training Their noise model is video specific and not relevant for image classification. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. 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. Work fast with our official CLI. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. Self-Training Noisy Student " " Self-Training . The comparison is shown in Table 9. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 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.
Summarization_self-training_with_noisy_student_improves_imagenet Learn more. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . Self-Training with Noisy Student Improves ImageNet Classification
A semi-supervised segmentation network based on noisy student learning Yalniz et al. 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. Proceedings of the eleventh annual conference on Computational learning theory, Proceedings of the IEEE conference on computer vision and pattern recognition, Empirical Methods in Natural Language Processing (EMNLP), Imagenet classification with deep convolutional neural networks, Domain adaptive transfer learning with specialist models, Thirty-Second AAAI Conference on Artificial Intelligence, Regularized evolution for image classifier architecture search, Inception-v4, inception-resnet and the impact of residual connections on learning. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. In particular, we first perform normal training with a smaller resolution for 350 epochs. For more information about the large architectures, please refer to Table7 in Appendix A.1. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. LeLinks:YouTube: https://www.youtube.com/c/yannickilcherTwitter: https://twitter.com/ykilcherDiscord: https://discord.gg/4H8xxDFBitChute: https://www.bitchute.com/channel/yannic-kilcherMinds: https://www.minds.com/ykilcherParler: https://parler.com/profile/YannicKilcherLinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/If you want to support me, the best thing to do is to share out the content :)If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):SubscribeStar (preferred to Patreon): https://www.subscribestar.com/yannickilcherPatreon: https://www.patreon.com/yannickilcherBitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cqEthereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9mMonero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. See This is probably because it is harder to overfit the large unlabeled dataset. labels, the teacher is not noised so that the pseudo labels are as good as Copyright and all rights therein are retained by authors or by other copyright holders. During this process, we kept increasing the size of the student model to improve the performance. Self-Training With Noisy Student Improves ImageNet Classification. 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. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. 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. First, we run an EfficientNet-B0 trained on ImageNet[69]. https://arxiv.org/abs/1911.04252. 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. combination of labeled and pseudo labeled images. on ImageNet ReaL. supervised model from 97.9% accuracy to 98.6% accuracy. Papers With Code is a free resource with all data licensed under. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. student is forced to learn harder from the pseudo labels. If nothing happens, download Xcode and try again. We improved it by adding noise to the student to learn beyond the teachers knowledge. Agreement NNX16AC86A, Is ADS down? 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. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet.
Self-Training With Noisy Student Improves ImageNet Classification 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. 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. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and 27.8 to 16.1. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Noise Self-training with Noisy Student 1. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images 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. 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. Self-training with Noisy Student improves ImageNet classification. ; 2006)[book reviews], Semi-supervised deep learning with memory, Proceedings of the European Conference on Computer Vision (ECCV), Xception: deep learning with depthwise separable convolutions, K. Clark, M. Luong, C. D. Manning, and Q. V. Le, Semi-supervised sequence modeling with cross-view training, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, AutoAugment: learning augmentation strategies from data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, RandAugment: practical data augmentation with no separate search, Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, Good semi-supervised learning that requires a bad gan, T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, A. Galloway, A. Golubeva, T. Tanay, M. Moussa, and G. W. Taylor, R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow, I. J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and harnessing adversarial examples, Semi-supervised learning by entropy minimization, Advances in neural information processing systems, K. Gu, B. Yang, J. Ngiam, Q. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. 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. Not only our method improves standard ImageNet accuracy, it also improves classification robustness on much harder test sets by large margins: ImageNet-A[25] top-1 accuracy from 16.6% to 74.2%, ImageNet-C[24] mean corruption error (mCE) from 45.7 to 31.2 and ImageNet-P[24] mean flip rate (mFR) from 27.8 to 16.1. 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. 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 obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. With Noisy Student, the model correctly predicts dragonfly for the image. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. over the JFT dataset to predict a label for each image. You signed in with another tab or window. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Why Self-training with Noisy Students beats SOTA Image classification In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. A common workaround is to use entropy minimization or ramp up the consistency loss. In other words, the student is forced to mimic a more powerful ensemble model. Semi-supervised medical image classification with relation-driven self-ensembling model. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. 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. Self-Training With Noisy Student Improves ImageNet Classification Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp.
Self-training with Noisy Student - A tag already exists with the provided branch name. w Summary of key results compared to previous state-of-the-art models. 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. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
We sample 1.3M images in confidence intervals. We thank the Google Brain team, Zihang Dai, Jeff Dean, Hieu Pham, Colin Raffel, Ilya Sutskever and Mingxing Tan for insightful discussions, Cihang Xie for robustness evaluation, Guokun Lai, Jiquan Ngiam, Jiateng Xie and Adams Wei Yu for feedbacks on the draft, Yanping Huang and Sameer Kumar for improving TPU implementation, Ekin Dogus Cubuk and Barret Zoph for help with RandAugment, Yanan Bao, Zheyun Feng and Daiyi Peng for help with the JFT dataset, Olga Wichrowska and Ola Spyra for help with infrastructure. 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. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. Le. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. If nothing happens, download Xcode and try again. 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. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Compared to consistency training[45, 5, 74], the self-training / teacher-student framework is better suited for ImageNet because we can train a good teacher on ImageNet using label data. 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). This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. If nothing happens, download GitHub Desktop and try again. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 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. The inputs to the algorithm are both labeled and unlabeled images. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In terms of methodology, We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. 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. augmentation, dropout, stochastic depth to the student so that the noised 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. [57] used self-training for domain adaptation. 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.
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