We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. For classes where we have too many images, we take the images with the highest confidence. E. Arazo, D. Ortego, P. Albert, N. E. OConnor, and K. McGuinness, Pseudo-labeling and confirmation bias in deep semi-supervised learning, B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson, There are many consistent explanations of unlabeled data: why you should average, International Conference on Learning Representations, Advances in Neural Information Processing Systems, D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, MixMatch: a holistic approach to semi-supervised learning, Combining labeled and unlabeled data with co-training, C. Bucilu, R. Caruana, and A. Niculescu-Mizil, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Y. Carmon, A. Raghunathan, L. Schmidt, P. Liang, and J. C. Duchi, Unlabeled data improves adversarial robustness, Semi-supervised learning (chapelle, o. et al., eds. Parthasarathi et al. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet. The inputs to the algorithm are both labeled and unlabeled images. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. 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. The mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. 10687-10698). We find that Noisy Student is better with an additional trick: data balancing. 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. 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. Self-Training With Noisy Student Improves ImageNet Classification Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 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]). Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le Description: We present a simple self-training method that 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. We iterate this process by putting back the student as the teacher. To achieve strong results on ImageNet, the student model also needs to be large, typically larger than common vision models, so that it can leverage a large number of unlabeled images. We evaluate the best model, that achieves 87.4% top-1 accuracy, on three robustness test sets: ImageNet-A, ImageNet-C and ImageNet-P. ImageNet-C and P test sets[24] include images with common corruptions and perturbations such as blurring, fogging, rotation and scaling. The performance consistently drops with noise function removed. Our study shows that using unlabeled data improves accuracy and general robustness. These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. Noise Self-training with Noisy Student 1. 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.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 way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). 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. 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. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. To date (2020) we will introduce "Noisy Student Training", which is a state-of-the-art model.The idea is to extend self-training and Distillation, a paper that shows that by adding three noises and distilling multiple times, the student model will have better generalization performance than the teacher model. unlabeled images , . We then train a larger EfficientNet as a student model on the We present a simple self-training method that 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. https://arxiv.org/abs/1911.04252, Accompanying notebook and sources to "A Guide to Pseudolabelling: How to get a Kaggle medal with only one model" (Dec. 2020 PyData Boston-Cambridge Keynote), Deep learning has shown remarkable successes in image recognition in recent years[35, 66, 62, 23, 69]. It 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. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Edit social preview. [68, 24, 55, 22]. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. In contrast, the predictions of the model with Noisy Student remain quite stable. Code for Noisy Student Training. This material is presented to ensure timely dissemination of scholarly and technical work. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Self-training with Noisy Student improves ImageNet classification. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. The architectures for the student and teacher models can be the same or different. IEEE Trans. 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. First, we run an EfficientNet-B0 trained on ImageNet[69]. It is expensive and must be done with great care. The paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task is revisited, and a simple recipe that is called Big Transfer (BiT) is created, which achieves strong performance on over 20 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. (or is it just me), Smithsonian Privacy 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. 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. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Our main results are shown in Table1. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. There was a problem preparing your codespace, please try again. The results also confirm that vision models can benefit from Noisy Student even without iterative training. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. 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. Flip probability is the probability that the model changes top-1 prediction for different perturbations. In particular, we first perform normal training with a smaller resolution for 350 epochs. Do better imagenet models transfer better? For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. The main use case of knowledge distillation is model compression by making the student model smaller. Our procedure went as follows. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. 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. Notice, Smithsonian Terms of Code for Noisy Student Training. Test images on ImageNet-P underwent different scales of perturbations. The main difference between our method and knowledge distillation is that knowledge distillation does not consider unlabeled data and does not aim to improve the student model. We use stochastic depth[29], dropout[63] and RandAugment[14]. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale.