Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Add a We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 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]). In terms of methodology, Astrophysical Observatory. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Finally, in the above, we say that the pseudo labels can be soft or hard. Self-Training With Noisy Student Improves ImageNet Classification C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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 sign in Self-training with Noisy Student improves ImageNet classification 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. 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 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 abundance of data on the internet is vast. 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.

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