If you get a better model, you can use the model to predict pseudo-labels on the filtered data. However, in the case with 130M unlabeled images, with noise function removed, the performance is still improved to 84.3% from 84.0% when compared to the supervised baseline. Self-Training With Noisy Student Improves ImageNet Classification @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019 . to use Codespaces. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. In other words, the student is forced to mimic a more powerful ensemble model. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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. You signed in with another tab or window. 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. 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. We then use the teacher model to generate pseudo labels on unlabeled images. These significant gains in robustness in ImageNet-C and ImageNet-P are surprising because our models were not deliberately optimizing for robustness (e.g., via data augmentation). Computer Science - Computer Vision and Pattern Recognition. Self-Training Noisy Student " " Self-Training . Are labels required for improving adversarial robustness? 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 ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. 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. Self-training 1 2Self-training 3 4n What is Noisy Student? Self-training with Noisy Student improves ImageNet classification. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. Train a larger classifier on the combined set, adding noise (noisy student). We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. 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. Here we study how to effectively use out-of-domain data. 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. For example, with all noise removed, the accuracy drops from 84.9% to 84.3% in the case with 130M unlabeled images and drops from 83.9% to 83.2% in the case with 1.3M unlabeled images. In other words, small changes in the input image can cause large changes to the predictions. On . Code is available at https://github.com/google-research/noisystudent. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. unlabeled images. For labeled images, we use a batch size of 2048 by default and reduce the batch size when we could not fit the model into the memory. Finally, we iterate the algorithm a few times by treating the student as a teacher to generate new pseudo labels and train a new student. The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. Please 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. 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. In the following, we will first describe experiment details to achieve our results. sign in Their purpose is different from ours: to adapt a teacher model on one domain to another. This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. 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 Noisy Student Training is based on the self-training framework and trained with 4 simple steps: For ImageNet checkpoints trained by Noisy Student Training, please refer to the EfficientNet github. Are you sure you want to create this branch? A novel random matrix theory based damping learner for second order optimisers inspired by linear shrinkage estimation is developed, and it is demonstrated that the derived method works well with adaptive gradient methods such as Adam. 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. 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. 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. Self-training with Noisy Student improves ImageNet classification. On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. For more information about the large architectures, please refer to Table7 in Appendix A.1. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. We use a resolution of 800x800 in this experiment. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . Use Git or checkout with SVN using the web URL. Papers With Code is a free resource with all data licensed under. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. By clicking accept or continuing to use the site, you agree to the terms outlined in our. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). We determine number of training steps and the learning rate schedule by the batch size for labeled images. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 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. Our main results are shown in Table1. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Are you sure you want to create this branch? Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . . We will then show our results on ImageNet and compare them with state-of-the-art models. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10687-10698, (2020 . We iterate this process by putting back the student as the teacher. The architectures for the student and teacher models can be the same or different. . self-mentoring outperforms data augmentation and self training. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. Self-training with Noisy Student improves ImageNet classification Abstract. Test images on ImageNet-P underwent different scales of perturbations. 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. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. It is expensive and must be done with great care. 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. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. In terms of methodology, Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. If nothing happens, download Xcode and try again. 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. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 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 top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. Self-training 27.8 to 16.1. Ranked #14 on Hence the total number of images that we use for training a student model is 130M (with some duplicated images). The performance drops when we further reduce it. 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. Since a teacher models confidence on an image can be a good indicator of whether it is an out-of-domain image, we consider the high-confidence images as in-domain images and the low-confidence images as out-of-domain images. supervised model from 97.9% accuracy to 98.6% accuracy. Self-training with Noisy Student improves ImageNet classication Qizhe Xie 1, Minh-Thang Luong , Eduard Hovy2, Quoc V. Le1 1Google Research, Brain Team, 2Carnegie Mellon University fqizhex, thangluong, qvlg@google.com, hovy@cmu.edu Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . We use the labeled images to train a teacher model using the standard cross entropy loss. There was a problem preparing your codespace, please try again. We present a simple self-training method that achieves 87.4 In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. For RandAugment, we apply two random operations with the magnitude set to 27. to noise the student. student is forced to learn harder from the pseudo labels. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. 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]. It can be seen that masks are useful in improving classification performance. . Infer labels on a much larger unlabeled dataset. Self-Training With Noisy Student Improves ImageNet Classification. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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. Parthasarathi et al. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. 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. The performance consistently drops with noise function removed. These CVPR 2020 papers are the Open Access versions, provided by the. In particular, we set the survival probability in stochastic depth to 0.8 for the final layer and follow the linear decay rule for other layers. 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. augmentation, dropout, stochastic depth to the student so that the noised [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . sign in Classification of Socio-Political Event Data, SLADE: A Self-Training Framework For Distance Metric Learning, Self-Training with Differentiable Teacher, https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. combination of labeled and pseudo labeled images. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. putting back the student as the teacher. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . [68, 24, 55, 22]. 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. 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. Self-training first uses labeled data to train a good teacher model, then use the teacher model to label unlabeled data and finally use the labeled data and unlabeled data to jointly train a student model. and surprising gains on robustness and adversarial benchmarks. As shown in Table3,4 and5, when compared with the previous state-of-the-art model ResNeXt-101 WSL[44, 48] trained on 3.5B weakly labeled images, Noisy Student yields substantial gains on robustness datasets.
Kent State Athletic Department Salaries, Robin Wall Kimmerer Daughters, Articles S