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. Noisy Student leads to significant improvements across all model sizes for EfficientNet. 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. We start with the 130M unlabeled images and gradually reduce the number of images. There was a problem preparing your codespace, please try again. 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. Due to duplications, there are only 81M unique images among these 130M images. 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]. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Self-training with Noisy Student improves ImageNet classification In terms of methodology, Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. Self-training 1 2Self-training 3 4n What is Noisy Student? The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. Please possible. 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 Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. First, we run an EfficientNet-B0 trained on ImageNet[69]. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. Code is available at https://github.com/google-research/noisystudent. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. over the JFT dataset to predict a label for each image. Whether the model benefits from more unlabeled data depends on the capacity of the model since a small model can easily saturate, while a larger model can benefit from more data. Noisy StudentImageNetEfficientNet-L2state-of-the-art. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment.

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