Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). 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%. (or is it just me), Smithsonian Privacy First, a teacher model is trained in a supervised fashion. 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 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. In terms of methodology, As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. . The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. combination of labeled and pseudo labeled images. 27.8 to 16.1. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. We use stochastic depth[29], dropout[63] and RandAugment[14]. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. 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 present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. [57] used self-training for domain adaptation. 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. Chowdhury et al. 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. Are labels required for improving adversarial robustness? However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. - : self-training_with_noisy_student_improves_imagenet_classification 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. Self-training with Noisy Student improves ImageNet classification. task. 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. Self-training with Noisy Student improves ImageNet classification. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Yalniz et al. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. 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. In particular, we first perform normal training with a smaller resolution for 350 epochs. 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. We start with the 130M unlabeled images and gradually reduce the number of images. This is probably because it is harder to overfit the large unlabeled dataset. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. student is forced to learn harder from the pseudo labels. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. 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. 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. 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. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. Self-Training with Noisy Student Improves ImageNet Classification We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. By showing the models only labeled images, we limit ourselves from making use of unlabeled images available in much larger quantities to improve accuracy and robustness of state-of-the-art models. Our main results are shown in Table1. 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. Infer labels on a much larger unlabeled dataset. Due to duplications, there are only 81M unique images among these 130M images. Notice, Smithsonian Terms of These CVPR 2020 papers are the Open Access versions, provided by the. This model investigates a new method. 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). If you get a better model, you can use the model to predict pseudo-labels on the filtered data. possible. 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. Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. 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. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. The abundance of data on the internet is vast. 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 this process, we kept increasing the size of the student model to improve the performance. 10687-10698 Abstract 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). A common workaround is to use entropy minimization or ramp up the consistency loss. Imaging, 39 (11) (2020), pp. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. The algorithm is basically self-training, a method in semi-supervised learning (. Self-training 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. Noise Self-training with Noisy Student 1. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. 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. For instance, on the right column, as the image of the car undergone a small rotation, the standard model changes its prediction from racing car to car wheel to fire engine. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. This material is presented to ensure timely dissemination of scholarly and technical work. In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. 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. 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. If nothing happens, download GitHub Desktop and try again. Our procedure went as follows. 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. (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. The baseline model achieves an accuracy of 83.2. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. Test images on ImageNet-P underwent different scales of perturbations. The abundance of data on the internet is vast. There was a problem preparing your codespace, please try again. If nothing happens, download GitHub Desktop and try again. Learn more. In other words, the student is forced to mimic a more powerful ensemble model. self-mentoring outperforms data augmentation and self training. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Abdominal organ segmentation is very important for clinical applications. 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 study how to effectively use out-of-domain data. 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. 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]. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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