Please refer to [24] for details about mCE and AlexNets error rate. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. Please Learn more. 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). The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Zoph et al. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. It 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. See If you get a better model, you can use the model to predict pseudo-labels on the filtered data. 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. . For instance, on ImageNet-A, Noisy Student achieves 74.2% top-1 accuracy which is approximately 57% more accurate than the previous state-of-the-art model. We start with the 130M unlabeled images and gradually reduce the number of images. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. IEEE Trans. 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 architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. We use the labeled images to train a teacher model using the standard cross entropy loss. 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. . 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. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Self-training 1 2Self-training 3 4n What is Noisy Student? Infer labels on a much larger unlabeled dataset. 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. 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. Flip probability is the probability that the model changes top-1 prediction for different perturbations. 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. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. 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. Noise Self-training with Noisy Student 1. 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. to noise the student. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. The abundance of data on the internet is vast. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Train a larger classifier on the combined set, adding noise (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. We use the standard augmentation instead of RandAugment in this experiment. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Edit social preview. . A tag already exists with the provided branch name. You signed in with another tab or window. 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. Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. Self-training with Noisy Student improves ImageNet classification. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. 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. 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. unlabeled images , . In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. 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. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Papers With Code is a free resource with all data licensed under. Code is available at https://github.com/google-research/noisystudent. In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. 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. Code for Noisy Student Training. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Different kinds of noise, however, may have different effects. We use stochastic depth[29], dropout[63] and RandAugment[14]. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). The main difference between Data Distillation and our method is that we use the noise to weaken the student, which is the opposite of their approach of strengthening the teacher by ensembling. 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. 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]. 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. 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. Their main goal is to find a small and fast model for deployment. 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. Soft pseudo labels lead to better performance for low confidence data. 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 work proposes a novel architectural unit, which is term the Squeeze-and-Excitation (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and shows that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. Their purpose is different from ours: to adapt a teacher model on one domain to another. 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. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. 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. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. 10687-10698). Noisy StudentImageNetEfficientNet-L2state-of-the-art. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. [^reference-9] [^reference-10] A critical insight was to . 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. 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. Here we study how to effectively use out-of-domain data. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. Different types of. 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 do not tune these hyperparameters extensively since our method is highly robust to them. . The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. Semi-supervised medical image classification with relation-driven self-ensembling model. We then select images that have confidence of the label higher than 0.3. unlabeled images. 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. Noisy Student Training is a semi-supervised learning approach. 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. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . Noisy Students performance improves with more unlabeled data. labels, the teacher is not noised so that the pseudo labels are as good as This model investigates a new method. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. However, manually annotating organs from CT scans is time . If nothing happens, download Xcode and try again. (using extra training data). Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. There was a problem preparing your codespace, please try again. Infer labels on a much larger unlabeled dataset. ImageNet-A top-1 accuracy from 16.6 Iterative training is not used here for simplicity. The accuracy is improved by about 10% in most settings. Use, Smithsonian This invariance constraint reduces the degrees of freedom in the model. The comparison is shown in Table 9. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. CLIP (Contrastive Language-Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.The idea of zero-data learning dates back over a decade [^reference-8] but until recently was mostly studied in computer vision as a way of generalizing to unseen object categories. 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. Next, a larger student model is trained on the combination of all data and achieves better performance than the teacher by itself.OUTLINE:0:00 - Intro \u0026 Overview1:05 - Semi-Supervised \u0026 Transfer Learning5:45 - Self-Training \u0026 Knowledge Distillation10:00 - Noisy Student Algorithm Overview20:20 - Noise Methods22:30 - Dataset Balancing25:20 - Results30:15 - Perturbation Robustness34:35 - Ablation Studies39:30 - Conclusion \u0026 CommentsPaper: https://arxiv.org/abs/1911.04252Code: https://github.com/google-research/noisystudentModels: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnetAbstract:We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Self-Training Noisy Student " " Self-Training . corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from We used the version from [47], which filtered the validation set of ImageNet. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. 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. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. ; 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. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. In other words, the student is forced to mimic a more powerful ensemble model. 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 By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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]. 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 results also confirm that vision models can benefit from Noisy Student even without iterative training. These CVPR 2020 papers are the Open Access versions, provided by the. sign in Hence we use soft pseudo labels for our experiments unless otherwise specified. We will then show our results on ImageNet and compare them with state-of-the-art models. 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%. - : self-training_with_noisy_student_improves_imagenet_classification Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. But during the learning of the student, we inject noise such as data The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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 labeled images, we use a batch size of 2048 by default and reduce the batch size when we could not fit the model into the memory. 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. But training robust supervised learning models is requires this step. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. combination of labeled and pseudo labeled images. As can be seen from the figure, our model with Noisy Student makes correct predictions for images under severe corruptions and perturbations such as snow, motion blur and fog, while the model without Noisy Student suffers greatly under these conditions. Chowdhury et al. It is expensive and must be done with great care. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. For RandAugment, we apply two random operations with the magnitude set to 27. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. We iterate this process by putting back the student as the teacher. 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. 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. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Noisy Student leads to significant improvements across all model sizes for EfficientNet. Med. Agreement NNX16AC86A, Is ADS down? Self-training The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. 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. Imaging, 39 (11) (2020), pp. Work fast with our official CLI. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). 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. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. 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 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.
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