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. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. To achieve this result, we first train an EfficientNet model on labeled 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]. Noisy Student Training seeks to improve on self-training and distillation in two ways. Self-Training With Noisy Student Improves ImageNet Classification 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]. Self-Training With Noisy Student Improves ImageNet Classification Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. The 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. This invariance constraint reduces the degrees of freedom in the model. Self-Training With Noisy Student Improves ImageNet Classification Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. . on ImageNet ReaL. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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%. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. 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. PDF Self-Training with Noisy Student Improves ImageNet Classification 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. 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. 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, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. In the following, we will first describe experiment details to achieve our results. Test images on ImageNet-P underwent different scales of perturbations. 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. Yalniz et al. On robustness test sets, it improves ImageNet-A top . Notice, Smithsonian Terms of Code for Noisy Student Training. Astrophysical Observatory. For more information about the large architectures, please refer to Table7 in Appendix A.1. Self-Training With Noisy Student Improves ImageNet Classification. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. However an important requirement for Noisy Student to work well is that the student model needs to be sufficiently large to fit more data (labeled and pseudo labeled). 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. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. We use the same architecture for the teacher and the student and do not perform iterative training. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. But training robust supervised learning models is requires this step. It is expensive and must be done with great care. A common workaround is to use entropy minimization or ramp up the consistency loss. 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. 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. Infer labels on a much larger unlabeled dataset. Are you sure you want to create this branch? You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Use Git or checkout with SVN using the web URL. Use Git or checkout with SVN using the web URL. In contrast, the predictions of the model with Noisy Student remain quite stable. We iterate this process by putting back the student as the teacher. Work fast with our official CLI. 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. 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. 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 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. Our procedure went as follows. 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. You signed in with another tab or window. tsai - Noisy student This attack performs one gradient descent step on the input image[20] with the update on each pixel set 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. 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. Self-Training : Noisy Student : 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. 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. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. 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_classification. 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. . This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. We use stochastic depth[29], dropout[63] and RandAugment[14]. 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. We use the labeled images to train a teacher model using the standard cross entropy loss. Their noise model is video specific and not relevant for image classification. Similar to[71], we fix the shallow layers during finetuning. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Noisy Student Explained | Papers With Code 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. 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. Self-training with Noisy Student - Medium 27.8 to 16.1. 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. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . See 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. Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. The comparison is shown in Table 9. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. Hence we use soft pseudo labels for our experiments unless otherwise specified. 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. A tag already exists with the provided branch name. 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. 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. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. unlabeled images , . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . We use EfficientNet-B4 as both the teacher and the student. 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. The main difference between our method and knowledge distillation is that knowledge distillation does not consider unlabeled data and does not aim to improve the student model. Semi-supervised medical image classification with relation-driven self-ensembling model. https://arxiv.org/abs/1911.04252. et al. 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]. 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. and surprising gains on robustness and adversarial benchmarks. Their main goal is to find a small and fast model for deployment. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher 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 Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. 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. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Soft pseudo labels lead to better performance for low confidence data. . For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 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. 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 . Significantly, after using the masks generated by student-SN, the classification performance improved by 0.9 of AC, 0.7 of SE, and 0.9 of AUC. augmentation, dropout, stochastic depth to the student so that the noised 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. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Train a larger classifier on the combined set, adding noise (noisy student). 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. Due to duplications, there are only 81M unique images among these 130M images. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 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. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Self-Training With Noisy Student Improves ImageNet Classification to use Codespaces. 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. 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). First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Please During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Train a larger classifier on the combined set, adding noise (noisy student). Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet Please refer to [24] for details about mCE and AlexNets error rate. For example, with all noise removed, the accuracy drops from 84.9% to 84.3% in the case with 130M unlabeled images and drops from 83.9% to 83.2% in the case with 1.3M unlabeled images. Noisy Student Training is a semi-supervised learning approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. The performance drops when we further reduce it. This is probably because it is harder to overfit the large unlabeled dataset. Agreement NNX16AC86A, Is ADS down? We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. [^reference-9] [^reference-10] A critical insight was to . 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. Noisy Student (EfficientNet) - huggingface.co 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. For classes where we have too many images, we take the images with the highest confidence. 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. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. 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. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Papers With Code is a free resource with all data licensed under. A tag already exists with the provided branch name. Noisy Students performance improves with more unlabeled data. 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. Self-training with Noisy Student improves ImageNet classification Especially unlabeled images are plentiful and can be collected with ease. [68, 24, 55, 22]. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline.
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