self training with noisy student improves imagenet classification

The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Notice, Smithsonian Terms of Noise Self-training with Noisy Student 1. 3429-3440. . This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. Self-Training for Natural Language Understanding! 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 . Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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. Summarization_self-training_with_noisy_student_improves_imagenet Self-training is a form of semi-supervised learning [10] which attempts to leverage unlabeled data to improve classification performance in the limited data regime. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. 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 We iterate this process by putting back the student as the teacher. 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. 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. 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]. 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. Edit social preview. For RandAugment, we apply two random operations with the magnitude set to 27. . 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 . 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. task. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Why Self-training with Noisy Students beats SOTA Image classification The width. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. We use the same architecture for the teacher and the student and do not perform iterative training. We iterate this process by putting back the student as the teacher. 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. 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 Train a larger classifier on the combined set, adding noise (noisy student). 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. It is expensive and must be done with great care. A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. 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. The performance consistently drops with noise function removed. on ImageNet ReaL This invariance constraint reduces the degrees of freedom in the model. Please Self-training with Noisy Student improves ImageNet classification We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Noisy Student can still improve the accuracy to 1.6%. A common workaround is to use entropy minimization or ramp up the consistency loss. Self-training with Noisy Student improves ImageNet classification Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a Self-training with noisy student improves imagenet classification. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Noisy Students performance improves with more unlabeled data. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from We use the labeled images to train a teacher model using the standard cross entropy loss. If nothing happens, download GitHub Desktop and try again. 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). Self-training with Noisy Student improves ImageNet classification Self-training with Noisy Student improves ImageNet classification. 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. Use, Smithsonian In other words, small changes in the input image can cause large changes to the predictions. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. 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. Noisy Student Training seeks to improve on self-training and distillation in two ways. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. combination of labeled and pseudo labeled 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. Please refer to [24] for details about mCE and AlexNets error rate. Self-Training With Noisy Student Improves ImageNet Classification IEEE Trans. The architectures for the student and teacher models can be the same or different. First, a teacher model is trained in a supervised fashion. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. 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. A tag already exists with the provided branch name. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Flip probability is the probability that the model changes top-1 prediction for different perturbations. On, International journal of molecular sciences. As shown in Table3,4 and5, when compared with the previous state-of-the-art model ResNeXt-101 WSL[44, 48] trained on 3.5B weakly labeled images, Noisy Student yields substantial gains on robustness datasets. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. 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. On . Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Use Git or checkout with SVN using the web URL. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. The algorithm is basically self-training, a method in semi-supervised learning (. 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. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Code is available at https://github.com/google-research/noisystudent. 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. 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. over the JFT dataset to predict a label for each image. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. 27.8 to 16.1. 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. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Especially unlabeled images are plentiful and can be collected with ease. 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. 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. Noisy Student Training is a semi-supervised learning approach. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . For classes where we have too many images, we take the images with the highest confidence. 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. 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 a simple self-training method that achieves 87.4 Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 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. The baseline model achieves an accuracy of 83.2. Our work is based on self-training (e.g.,[59, 79, 56]). 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. 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. Here we study how to effectively use out-of-domain data. 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. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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. Self-training with Noisy Student improves ImageNet classification Original paper: https://arxiv.org/pdf/1911.04252.pdf Authors: Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le HOYA012 Introduction EfficientNet ImageNet SOTA EfficientNet We use a resolution of 800x800 in this experiment. For instance, on the right column, as the image of the car undergone a small rotation, the standard model changes its prediction from racing car to car wheel to fire engine. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. [57] used self-training for domain adaptation. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. 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. 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. But during the learning of the student, we inject noise such as data The most interesting image is shown on the right of the first row. 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 pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. https://arxiv.org/abs/1911.04252. Code for Noisy Student Training. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Ranked #14 on There was a problem preparing your codespace, please try again. The best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. The abundance of data on the internet is vast. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative 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. Hence we use soft pseudo labels for our experiments unless otherwise specified. In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. Self-Training Noisy Student " " Self-Training . Noisy Student Training is based on the self-training framework and trained with 4 simple steps: For ImageNet checkpoints trained by Noisy Student Training, please refer to the EfficientNet github. Self-training with Noisy Student - An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). Chowdhury et al. self-mentoring outperforms data augmentation and self training. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. You signed in with another tab or window. Imaging, 39 (11) (2020), pp. In other words, the student is forced to mimic a more powerful ensemble model. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. unlabeled images. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. 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. This material is presented to ensure timely dissemination of scholarly and technical work. 10687-10698). This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. By clicking accept or continuing to use the site, you agree to the terms outlined in our. on ImageNet, which is 1.0 Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. (using extra training data). Astrophysical Observatory. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. Papers With Code is a free resource with all data licensed under. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. 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. Here we show the evidence in Table 6, noise such as stochastic depth, dropout and data augmentation plays an important role in enabling the student model to perform better than the teacher. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. 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. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. 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. During this process, we kept increasing the size of the student model to improve the performance. Different kinds of noise, however, may have different effects. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. Do imagenet classifiers generalize to imagenet? ImageNet-A top-1 accuracy from 16.6 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. Work fast with our official CLI. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Noisy StudentImageNetEfficientNet-L2state-of-the-art. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Self-Training with Noisy Student Improves ImageNet Classification If nothing happens, download Xcode and try again. A tag already exists with the provided branch name. 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. Iterative training is not used here for simplicity. Then, that teacher is used to label the unlabeled data. There was a problem preparing your codespace, please try again. Their noise model is video specific and not relevant for image classification. on ImageNet ReaL. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks.

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