self training with noisy student improves imagenet classification

mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. The most interesting image is shown on the right of the first row. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. A number of studies, e.g. 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" pytorch implementation. 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. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. When data augmentation noise is used, the student must ensure that a translated image, for example, should have the same category with a non-translated image. We then use the teacher model to generate pseudo labels on unlabeled images. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards 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. Self-Training With Noisy Student Improves ImageNet Classification Our procedure went as follows. [^reference-9] [^reference-10] A critical insight was to . Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. The abundance of data on the internet is vast. Self-Training With Noisy Student Improves ImageNet Classification. It is expensive and must be done with great care. For more information about the large architectures, please refer to Table7 in Appendix A.1. Self-Training for Natural Language Understanding! Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen, GPipe: efficient training of giant neural networks using pipeline parallelism, A. Iscen, G. Tolias, Y. Avrithis, and O. Computer Science - Computer Vision and Pattern Recognition. Their purpose is different from ours: to adapt a teacher model on one domain to another. We use stochastic depth[29], dropout[63] and RandAugment[14]. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. 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 method and knowledge distillation is that knowledge distillation does not consider unlabeled data and does not aim to improve the student model. (Submitted on 11 Nov 2019) We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. 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]. 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. Code for Noisy Student Training. 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. The performance consistently drops with noise function removed. w Summary of key results compared to previous state-of-the-art models. We sample 1.3M images in confidence intervals. Self-Training With Noisy Student Improves ImageNet Classification 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. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. supervised model from 97.9% accuracy to 98.6% accuracy. sign in student is forced to learn harder from the pseudo labels. Self-training with Noisy Student improves ImageNet classification We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Self-training with Noisy Student improves ImageNet classification (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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. 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 baseline model achieves an accuracy of 83.2. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Flip probability is the probability that the model changes top-1 prediction for different perturbations. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Ranked #14 on Agreement NNX16AC86A, Is ADS down? We also study the effects of using different amounts of unlabeled data. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. We iterate this process by putting back the student as the teacher. SelfSelf-training with Noisy Student improves ImageNet classification ImageNet-A top-1 accuracy from 16.6 Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. For simplicity, we experiment with using 1128,164,132,116,14 of the whole data by uniformly sampling images from the the unlabeled set though taking the images with highest confidence leads to better results. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. Finally, in the above, we say that the pseudo labels can be soft or hard. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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 top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. (or is it just me), Smithsonian Privacy During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. https://arxiv.org/abs/1911.04252. On . Distillation Survey : Noisy Student | 9to5Tutorial Code for Noisy Student Training. Yalniz et al. Please refer to [24] for details about mFR and AlexNets flip probability. 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. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. 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. Models are available at this https URL. all 12, Image Classification When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single 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 investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. This invariance constraint reduces the degrees of freedom in the model. A tag already exists with the provided branch name. Our work is based on self-training (e.g.,[59, 79, 56]). 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. Are you sure you want to create this branch? Different kinds of noise, however, may have different effects. Summarization_self-training_with_noisy_student_improves_imagenet (using extra training data). 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. 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. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. 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. These CVPR 2020 papers are the Open Access versions, provided by the. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. 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. on ImageNet, which is 1.0 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 . corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Hence we use soft pseudo labels for our experiments unless otherwise specified. This model investigates a new method. Self-Training Noisy Student " " Self-Training . Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. 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. Learn more. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model 2023.3.1_2 - 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. Self-training with Noisy Student - We find that Noisy Student is better with an additional trick: data balancing. 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 . 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. Add a Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Self-training with Noisy Student - Medium 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]. 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. Although they have produced promising results, in our preliminary experiments, consistency regularization works less well on ImageNet because consistency regularization in the early phase of ImageNet training regularizes the model towards high entropy predictions, and prevents it from achieving good accuracy. 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. Their main goal is to find a small and fast model for deployment. 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 first uses labeled data to train a good teacher model, then use the teacher model to label unlabeled data and finally use the labeled data and unlabeled data to jointly train a student model. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. International Conference on Machine Learning, Learning extraction patterns for subjective expressions, Proceedings of the 2003 conference on Empirical methods in natural language processing, A. Roy Chowdhury, P. Chakrabarty, A. Singh, S. Jin, H. Jiang, L. Cao, and E. G. Learned-Miller, Automatic adaptation of object detectors to new domains using self-training, T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, Probability of error of some adaptive pattern-recognition machines, W. Shi, Y. Gong, C. Ding, Z. MaXiaoyu Tao, and N. Zheng, Transductive semi-supervised deep learning using min-max features, C. Simon-Gabriel, Y. Ollivier, L. Bottou, B. Schlkopf, and D. Lopez-Paz, First-order adversarial vulnerability of neural networks and input dimension, Very deep convolutional networks for large-scale image recognition, N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. Use, Smithsonian We use EfficientNet-B4 as both the teacher and the student. On robustness test sets, it improves In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Self-training with Noisy Student improves ImageNet classification 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.

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