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. On . As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. For classes where we have too many images, we take the images with the highest confidence. In terms of methodology, [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . 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 . task. Please refer to [24] for details about mFR and AlexNets flip probability. . To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. 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 Train a classifier on labeled data (teacher). Learn more. Train a classifier on labeled data (teacher). Notice, Smithsonian Terms of Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. 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. 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]. We first report the validation set accuracy on the ImageNet 2012 ILSVRC challenge prediction task as commonly done in literature[35, 66, 23, 69] (see also [55]). Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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. 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. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. We find that Noisy Student is better with an additional trick: data balancing. IEEE Transactions on Pattern Analysis and Machine Intelligence. To intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct. 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. The width. 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). First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Our procedure went as follows. 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. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. A common workaround is to use entropy minimization or ramp up the consistency loss. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. 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. Models are available at this https URL. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of 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. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. self-mentoring outperforms data augmentation and self training. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . You signed in with another tab or window. Infer labels on a much larger unlabeled dataset. 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. 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. possible. 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. Edit social preview. But during the learning of the student, we inject noise such as data We apply dropout to the final classification layer with a dropout rate of 0.5. ImageNet-A top-1 accuracy from 16.6 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. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Image Classification Noisy Student leads to significant improvements across all model sizes for EfficientNet. 10687-10698 Abstract The performance consistently drops with noise function removed. Use Git or checkout with SVN using the web URL. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. We then perform data filtering and balancing on this corpus. - : self-training_with_noisy_student_improves_imagenet_classification On, International journal of molecular sciences. 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 a semi-supervised learning approach. Infer labels on a much larger unlabeled dataset. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. unlabeled images. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Summarization_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. We iterate this process by putting back the student as the teacher. 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. Different kinds of noise, however, may have different effects. If nothing happens, download GitHub Desktop and try again. Especially unlabeled images are plentiful and can be collected with ease. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Self-training with Noisy Student. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. 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. We iterate this process by putting back the student as the teacher. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. over the JFT dataset to predict a label for each image. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). 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. 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. 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. 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. 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. This is probably because it is harder to overfit the large unlabeled dataset. This material is presented to ensure timely dissemination of scholarly and technical work. 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. We also list EfficientNet-B7 as a reference. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. on ImageNet ReaL Do better imagenet models transfer better? 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. 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. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. Due to duplications, there are only 81M unique images among these 130M images. Papers With Code is a free resource with all data licensed under. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. 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. Noisy Student Training seeks to improve on self-training and distillation in two ways. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 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. We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled 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. 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. We use the same architecture for the teacher and the student and do not perform iterative training. 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. Please These CVPR 2020 papers are the Open Access versions, provided by the. 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. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from Are you sure you want to create this branch? Self-training with noisy student improves imagenet classification. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. 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. Please refer to [24] for details about mCE and AlexNets error rate. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. 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. Please Different types of. 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]. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. , have shown that computer vision models lack robustness. Are labels required for improving adversarial robustness? 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]. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. We then select images that have confidence of the label higher than 0.3. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). On robustness test sets, it improves We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. 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]. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. labels, the teacher is not noised so that the pseudo labels are as good as Work fast with our official CLI. The most interesting image is shown on the right of the first row. Self-Training Noisy Student " " Self-Training . Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. 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. 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. 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]. 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. . 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. 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. Our main results are shown in Table1. In contrast, the predictions of the model with Noisy Student remain quite stable. 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. 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. 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. augmentation, dropout, stochastic depth to the student so that the noised Noisy Student can still improve the accuracy to 1.6%. The algorithm is basically self-training, a method in semi-supervised learning (. 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. Self-training with Noisy Student improves ImageNet classification. to use Codespaces. 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. Copyright and all rights therein are retained by authors or by other copyright holders. 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. Then, that teacher is used to label the unlabeled data. [68, 24, 55, 22]. With Noisy Student, the model correctly predicts dragonfly for the image. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Their main goal is to find a small and fast model for deployment. 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. Semi-supervised medical image classification with relation-driven self-ensembling model. Agreement NNX16AC86A, Is ADS down? While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. Especially unlabeled images are plentiful and can be collected with ease. EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. We then use the teacher model to generate pseudo labels on unlabeled images. In other words, small changes in the input image can cause large changes to the predictions. We sample 1.3M images in confidence intervals. (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. During the generation of the pseudo The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. 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. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. [^reference-9] [^reference-10] A critical insight was to . Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Self-training with Noisy Student improves ImageNet classification. Noisy StudentImageNetEfficientNet-L2state-of-the-art. The baseline model achieves an accuracy of 83.2. The results also confirm that vision models can benefit from Noisy Student even without iterative training. 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%. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The accuracy is improved by about 10% in most settings. 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. [57] used self-training for domain adaptation. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. 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. 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. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a Hence we use soft pseudo labels for our experiments unless otherwise specified. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. We then train a larger EfficientNet as a student model on the Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models.
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