We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. We thank the Google Brain team, Zihang Dai, Jeff Dean, Hieu Pham, Colin Raffel, Ilya Sutskever and Mingxing Tan for insightful discussions, Cihang Xie for robustness evaluation, Guokun Lai, Jiquan Ngiam, Jiateng Xie and Adams Wei Yu for feedbacks on the draft, Yanping Huang and Sameer Kumar for improving TPU implementation, Ekin Dogus Cubuk and Barret Zoph for help with RandAugment, Yanan Bao, Zheyun Feng and Daiyi Peng for help with the JFT dataset, Olga Wichrowska and Ola Spyra for help with infrastructure. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Their purpose is different from ours: to adapt a teacher model on one domain to another. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Summarization_self-training_with_noisy_student_improves_imagenet_classification. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Are labels required for improving adversarial robustness? IEEE Trans. While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. During the generation of the pseudo Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). 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. 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. augmentation, dropout, stochastic depth to the student so that the noised As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 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. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. The inputs to the algorithm are both labeled and unlabeled images. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. We duplicate images in classes where there are not enough images. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 10687-10698). 10687-10698 Abstract We use the same architecture for the teacher and the student and do not perform iterative training. 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. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. For instance, on ImageNet-A, Noisy Student achieves 74.2% top-1 accuracy which is approximately 57% more accurate than the previous state-of-the-art model. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Specifically, we train the student model for 350 epochs for models larger than EfficientNet-B4, including EfficientNet-L0, L1 and L2 and train the student model for 700 epochs for smaller models. 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. Especially unlabeled images are plentiful and can be collected with ease. First, a teacher model is trained in a supervised fashion. The baseline model achieves an accuracy of 83.2. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Self-training with noisy student improves imagenet classification. 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. Similar to[71], we fix the shallow layers during finetuning. We use EfficientNet-B0 as both the teacher model and the student model and compare using Noisy Student with soft pseudo labels and hard pseudo labels. Astrophysical Observatory. 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. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Train a larger classifier on the combined set, adding noise (noisy student). About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . For more information about the large architectures, please refer to Table7 in Appendix A.1. 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. 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. Self-Training Noisy Student " " Self-Training . Do better imagenet models transfer better? https://arxiv.org/abs/1911.04252. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. For classes where we have too many images, we take the images with the highest confidence. This work proposes a novel architectural unit, which is term the Squeeze-and-Excitation (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and shows that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. In contrast, the predictions of the model with Noisy Student remain quite stable. Train a classifier on labeled data (teacher). Finally, in the above, we say that the pseudo labels can be soft or hard. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. Here we use unlabeled images to improve the state-of-the-art ImageNet accuracy and show that the accuracy gain has an outsized impact on robustness. 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. ImageNet . student is forced to learn harder from the pseudo labels. 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. (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. As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. 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. 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. Zoph et al. 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. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Self-training was previously used to improve ResNet-50 from 76.4% to 81.2% top-1 accuracy[76] which is still far from the state-of-the-art accuracy. The ONCE (One millioN sCenEs) dataset for 3D object detection in the autonomous driving scenario is introduced and a benchmark is provided in which a variety of self-supervised and semi- supervised methods on the ONCE dataset are evaluated. This is probably because it is harder to overfit the large unlabeled dataset. For RandAugment, we apply two random operations with the magnitude set to 27. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. If nothing happens, download GitHub Desktop and try again. 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. 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. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. The main use case of knowledge distillation is model compression by making the student model smaller. We iterate this process by putting back the student as the teacher. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). Self-Training With Noisy Student Improves ImageNet Classification Abstract: 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. Work fast with our official CLI. 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. In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 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. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from Self-training with Noisy Student improves ImageNet classification. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. ImageNet-A top-1 accuracy from 16.6 We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y. Please refer to [24] for details about mFR and AlexNets flip probability. self-mentoring outperforms data augmentation and self training. 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. Noisy Student leads to significant improvements across all model sizes for EfficientNet. Noisy Student Training seeks to improve on self-training and distillation in two ways. We start with the 130M unlabeled images and gradually reduce the number of 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].
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