In other words, small changes in the input image can cause large changes to the predictions. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Self-Training With Noisy Student Improves ImageNet Classification. In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher. 3.5B weakly labeled Instagram images. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. 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. 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 To achieve this result, we first train an EfficientNet model on labeled 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. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. 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. Iterative training is not used here for simplicity. Our main results are shown in Table1. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. 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. 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. 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. 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. We use the standard augmentation instead of RandAugment in this experiment. Copyright and all rights therein are retained by authors or by other copyright holders. Noisy StudentImageNetEfficientNet-L2state-of-the-art. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative In this section, we study the importance of noise and the effect of several noise methods used in our model. [68, 24, 55, 22]. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Due to duplications, there are only 81M unique images among these 130M images. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Self-training with Noisy Student improves ImageNet classification. 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. But during the learning of the student, we inject noise such as data Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Learn more. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. Self-Training Noisy Student " " Self-Training . We present a simple self-training method that achieves 87.4 We use stochastic depth[29], dropout[63] and RandAugment[14]. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. Work fast with our official CLI. Astrophysical Observatory. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Train a classifier on labeled data (teacher). 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. 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. It implements SemiSupervised Learning with Noise to create an Image Classification. Papers With Code is a free resource with all data licensed under. Self-training is a form of semi-supervised learning [10] which attempts to leverage unlabeled data to improve classification performance in the limited data regime. w Summary of key results compared to previous state-of-the-art models. A number of studies, e.g. 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 have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. On robustness test sets, it improves Notice, Smithsonian Terms of See First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. and surprising gains on robustness and adversarial benchmarks. on ImageNet, which is 1.0 Use Git or checkout with SVN using the web URL. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. We iterate this process by putting back the student as the teacher. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Noise Self-training with Noisy Student 1. 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. Finally, frameworks in semi-supervised learning also include graph-based methods [84, 73, 77, 33], methods that make use of latent variables as target variables [32, 42, 78] and methods based on low-density separation[21, 58, 15], which might provide complementary benefits to our method. We iterate this process by putting back the student as the teacher. We then use the teacher model to generate pseudo labels on unlabeled images. We then perform data filtering and balancing on this corpus. Their noise model is video specific and not relevant for image classification. 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. Further, Noisy Student outperforms the state-of-the-art accuracy of 86.4% by FixRes ResNeXt-101 WSL[44, 71] that requires 3.5 Billion Instagram images labeled with tags. We will then show our results on ImageNet and compare them with state-of-the-art models. (or is it just me), Smithsonian Privacy The abundance of data on the internet is vast. ImageNet images and use it as a teacher to generate pseudo labels on 300M . This model investigates a new method. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. Learn more. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. 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. task. It is expensive and must be done with great care. A common workaround is to use entropy minimization or ramp up the consistency loss. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Noisy Student Training is based on the self-training framework and trained with 4-simple steps: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. to noise the student. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. We used the version from [47], which filtered the validation set of ImageNet. Zoph et al. The results also confirm that vision models can benefit from Noisy Student even without iterative training. Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. When data augmentation noise is used, the student must ensure that a translated image, for example, should have the same category with a non-translated image. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. We then train a larger EfficientNet as a student model on the 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]. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images.