We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. [^reference-9] [^reference-10] A critical insight was to . 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. (using extra training data). ImageNet images and use it as a teacher to generate pseudo labels on 300M Self-training with Noisy Student improves ImageNet classification. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. putting back the student as the teacher. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. 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. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. augmentation, dropout, stochastic depth to the student so that the noised 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. During the generation of the pseudo Distillation Survey : Noisy Student | 9to5Tutorial Self-training with Noisy Student improves ImageNet classification These CVPR 2020 papers are the Open Access versions, provided by the. Med. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a Please On robustness test sets, it improves ImageNet-A top . We do not tune these hyperparameters extensively since our method is highly robust to them. Self-training with Noisy Student improves ImageNet classification First, we run an EfficientNet-B0 trained on ImageNet[69]. 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. 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. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . ; 2006)[book reviews], Semi-supervised deep learning with memory, Proceedings of the European Conference on Computer Vision (ECCV), Xception: deep learning with depthwise separable convolutions, K. Clark, M. Luong, C. D. Manning, and Q. V. Le, Semi-supervised sequence modeling with cross-view training, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, AutoAugment: learning augmentation strategies from data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, RandAugment: practical data augmentation with no separate search, Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, Good semi-supervised learning that requires a bad gan, T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, A. Galloway, A. Golubeva, T. Tanay, M. Moussa, and G. W. Taylor, R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow, I. J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and harnessing adversarial examples, Semi-supervised learning by entropy minimization, Advances in neural information processing systems, K. Gu, B. Yang, J. Ngiam, Q. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. 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. If nothing happens, download Xcode and try again. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. This material is presented to ensure timely dissemination of scholarly and technical work. In particular, we first perform normal training with a smaller resolution for 350 epochs. 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 use the labeled images to train a teacher model using the standard cross entropy loss. We use the standard augmentation instead of RandAugment in this experiment. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. . Code is available at https://github.com/google-research/noisystudent. Edit social preview. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. To achieve this result, we first train an EfficientNet model on labeled 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. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . We improved it by adding noise to the student to learn beyond the teachers knowledge. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. A semi-supervised segmentation network based on noisy student learning In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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. Self-Training With Noisy Student Improves ImageNet Classification Self-training with Noisy Student improves ImageNet classification. 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. Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. Le. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. For each class, we select at most 130K images that have the highest confidence. Agreement NNX16AC86A, Is ADS down? 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. Self-mentoring: : A new deep learning pipeline to train a self In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. 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. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. We start with the 130M unlabeled images and gradually reduce the number of images. We find that Noisy Student is better with an additional trick: data balancing. We present a simple self-training method that achieves 87.4 Self-training with Noisy Student improves ImageNet classification Abstract. This invariance constraint reduces the degrees of freedom in the model. Proceedings of the eleventh annual conference on Computational learning theory, Proceedings of the IEEE conference on computer vision and pattern recognition, Empirical Methods in Natural Language Processing (EMNLP), Imagenet classification with deep convolutional neural networks, Domain adaptive transfer learning with specialist models, Thirty-Second AAAI Conference on Artificial Intelligence, Regularized evolution for image classifier architecture search, Inception-v4, inception-resnet and the impact of residual connections on learning. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. 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. We iterate this process by putting back the student as the teacher. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. When the student model is deliberately noised it is actually trained to be consistent to the more powerful teacher model that is not noised when it generates pseudo labels. Here we study if it is possible to improve performance on small models by using a larger teacher model, since small models are useful when there are constraints for model size and latency in real-world applications. over the JFT dataset to predict a label for each image. Noisy Student Explained | Papers With Code Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Our procedure went as follows. Train a classifier on labeled data (teacher). Are labels required for improving adversarial robustness? In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. on ImageNet ReaL. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. Add a Self-training with Noisy Student improves ImageNet classification GitHub - google-research/noisystudent: Code for Noisy Student Training A number of studies, e.g. 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. A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. Noise Self-training with Noisy Student 1. Copyright and all rights therein are retained by authors or by other copyright holders. Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. 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. Are you sure you want to create this branch? 10687-10698). "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Work fast with our official CLI. Train a classifier on labeled data (teacher). 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. Train a larger classifier on the combined set, adding noise (noisy student). We used the version from [47], which filtered the validation set of ImageNet. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. 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. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 Chowdhury et al. 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. Ranked #14 on We then perform data filtering and balancing on this corpus. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. Self-Training Noisy Student " " Self-Training . For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. Code for Noisy Student Training. Image Classification Self-training with Noisy Student improves ImageNet classification For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Computer Science - Computer Vision and Pattern Recognition. 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. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. https://arxiv.org/abs/1911.04252. 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]. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). A common workaround is to use entropy minimization or ramp up the consistency loss. A tag already exists with the provided branch name. Summarization_self-training_with_noisy_student_improves_imagenet_classification. on ImageNet, which is 1.0 Their purpose is different from ours: to adapt a teacher model on one domain to another. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. If nothing happens, download Xcode and try again. et al. Here we study how to effectively use out-of-domain data. team using this approach not only surpasses the top-1 ImageNet accuracy of SOTA models by 1%, it also shows that the robustness of a model also improves. By clicking accept or continuing to use the site, you agree to the terms outlined in our. This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. Different kinds of noise, however, may have different effects. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Self-training with Noisy Student improves ImageNet classificationCVPR2020, Codehttps://github.com/google-research/noisystudent, Self-training, 1, 2Self-training, Self-trainingGoogleNoisy Student, Noisy Studentstudent modeldropout, stochastic depth andaugmentationteacher modelNoisy Noisy Student, Noisy Student, 1, JFT3ImageNetEfficientNet-B00.3130K130K, EfficientNetbaseline modelsEfficientNetresnet, EfficientNet-B7EfficientNet-L0L1L2, batchsize = 2048 51210242048EfficientNet-B4EfficientNet-L0l1L2350epoch700epoch, 2EfficientNet-B7EfficientNet-L0, 3EfficientNet-L0EfficientNet-L1L0, 4EfficientNet-L1EfficientNet-L2, student modelNoisy, noisystudent modelteacher modelNoisy, Noisy, Self-trainingaugmentationdropoutstochastic depth, Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores., 12/self-training-with-noisy-student-f33640edbab2, EfficientNet-L0EfficientNet-B7B7, EfficientNet-L1EfficientNet-L0, EfficientNetsEfficientNet-L1EfficientNet-L2EfficientNet-L2EfficientNet-B75.

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