During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 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. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Their noise model is video specific and not relevant for image classification. On, International journal of molecular sciences. 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. Infer labels on a much larger unlabeled dataset. 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. Code for Noisy Student Training. Image Classification mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. A number of studies, e.g. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. on ImageNet ReaL. Test images on ImageNet-P underwent different scales of perturbations. 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. In contrast, the predictions of the model with Noisy Student remain quite stable. 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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 call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. to use Codespaces. 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. Here we study how to effectively use out-of-domain data. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. 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 found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. Please The comparison is shown in Table 9. 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. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. labels, the teacher is not noised so that the pseudo labels are as good as 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. If you get a better model, you can use the model to predict pseudo-labels on the filtered 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 apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Computer Science - Computer Vision and Pattern Recognition. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Self-Training With Noisy Student Improves ImageNet Classification. Le. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. We improved it by adding noise to the student to learn beyond the teachers knowledge. 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. It is found that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. By clicking accept or continuing to use the site, you agree to the terms outlined in our. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. The performance drops when we further reduce it. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. 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. We duplicate images in classes where there are not enough images. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. 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. to use Codespaces. 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. We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. unlabeled images. Notice, Smithsonian Terms of Train a larger classifier on the combined set, adding noise (noisy student). It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images (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. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. We iterate this process by putting back the student as the teacher. On robustness test sets, it improves We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. The width. 10687-10698 Abstract We apply dropout to the final classification layer with a dropout rate of 0.5. Use, Smithsonian 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. This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. We then train a larger EfficientNet as a student model on the 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. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. task. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Soft pseudo labels lead to better performance for low confidence data. A tag already exists with the provided branch name. Add a This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. 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 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. student is forced to learn harder from the pseudo labels. The most interesting image is shown on the right of the first row. 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. In other words, small changes in the input image can cause large changes to the predictions. 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. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks. 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. Especially unlabeled images are plentiful and can be collected with ease. 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 use EfficientNet-B4 as both the teacher and the student. 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 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. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 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. Due to duplications, there are only 81M unique images among these 130M images.
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