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Noisy Student Training is a semi-supervised learning approach. Code for Noisy Student Training. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. Self-training with Noisy Student improves ImageNet classification. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. 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. 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. and surprising gains on robustness and adversarial benchmarks. We use EfficientNet-B4 as both the teacher and the student. We iterate this process by Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Noisy Student Training seeks to improve on self-training and distillation in two ways. 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. The learning rate starts at 0.128 for labeled batch size 2048 and decays by 0.97 every 2.4 epochs if trained for 350 epochs or every 4.8 epochs if trained for 700 epochs. Do imagenet classifiers generalize to imagenet? For more information about the large architectures, please refer to Table7 in Appendix A.1. 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. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. The results also confirm that vision models can benefit from Noisy Student even without iterative training. The main use case of knowledge distillation is model compression by making the student model smaller. The baseline model achieves an accuracy of 83.2. Use, Smithsonian Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . (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. [68, 24, 55, 22]. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. A tag already exists with the provided branch name. Flip probability is the probability that the model changes top-1 prediction for different perturbations. If nothing happens, download GitHub Desktop and try again. See 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 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. 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. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. ImageNet . They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. unlabeled images. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. When the student model is deliberately noised it is actually trained to be consistent to the more powerful teacher model that is not noised when it generates pseudo labels. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Are you sure you want to create this branch? 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. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. [^reference-9] [^reference-10] A critical insight was to . The best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. You signed in with another tab or window. We iterate this process by putting back the student as the teacher. Self-training with Noisy Student improves ImageNet classification In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. For classes where we have too many images, we take the images with the highest confidence. 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. After using the masks generated by teacher-SN, the classification performance improved by 0.2 of AC, 1.2 of SP, and 0.7 of AUC. , have shown that computer vision models lack robustness. During this process, we kept increasing the size of the student model to improve the performance. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. Self-training with Noisy Student improves ImageNet classification Models are available at this https URL. sign in These CVPR 2020 papers are the Open Access versions, provided by the. 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. Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Self-training with Noisy Student improves ImageNet classification. 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 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. Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). labels, the teacher is not noised so that the pseudo labels are as good as 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]. 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. Self-training with Noisy Student improves ImageNet classification. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. 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 Summarization_self-training_with_noisy_student_improves_imagenet 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. 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 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.
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