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 hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. A number of studies, e.g. Please refer to [24] for details about mCE and AlexNets error rate. During the generation of the pseudo Agreement NNX16AC86A, Is ADS down? These CVPR 2020 papers are the Open Access versions, provided by the. But during the learning of the student, we inject noise such as data 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. Our work is based on self-training (e.g.,[59, 79, 56]). 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. Works based on pseudo label[37, 31, 60, 1] are similar to self-training, but also suffers the same problem with consistency training, since it relies on a model being trained instead of a converged model with high accuracy to generate pseudo labels. 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. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Code is available at https://github.com/google-research/noisystudent. Especially unlabeled images are plentiful and can be collected with ease. Ranked #14 on Self-training with Noisy Student improves ImageNet classification. 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. to use Codespaces. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Use Git or checkout with SVN using the web URL. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. FixMatch-LS: Semi-supervised skin lesion classification with label One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. Although noise may appear to be limited and uninteresting, when it is applied to unlabeled data, it has a compound benefit of enforcing local smoothness in the decision function on both labeled and unlabeled data. If nothing happens, download Xcode and try again. Finally, in the above, we say that the pseudo labels can be soft or hard. Code is available at https://github.com/google-research/noisystudent. You signed in with another tab or window. 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. sign in It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images 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. 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. But training robust supervised learning models is requires this step. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Train a larger classifier on the combined set, adding noise (noisy student). If nothing happens, download GitHub Desktop and try again. 3429-3440. . 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 First, a teacher model is trained in a supervised fashion. 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. 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. 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. 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. In terms of methodology, Noisy Student leads to significant improvements across all model sizes for EfficientNet. We also study the effects of using different amounts of unlabeled data. 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. 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. It is experimentally validated that, for a target test resolution, using a lower train resolution offers better classification at test time, and a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ is proposed. To intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct. Due to duplications, there are only 81M unique images among these 130M images. We iterate this process by putting back the student as the teacher. Are you sure you want to create this branch? 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. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Different kinds of noise, however, may have different effects. We iterate this process by putting back the student as the teacher. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. This paper reviews the state-of-the-art in both the field of CNNs for image classification and object detection and Autonomous Driving Systems (ADSs) in a synergetic way including a comprehensive trade-off analysis from a human-machine perspective. 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. 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. 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. [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 this process, we kept increasing the size of the student model to improve the performance. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. CVPR 2020 Open Access Repository This invariance constraint reduces the degrees of freedom in the model. For unlabeled images, we set the batch size to be three times the batch size of labeled images for large models, including EfficientNet-B7, L0, L1 and L2. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). In particular, we first perform normal training with a smaller resolution for 350 epochs. Self-training with Noisy Student improves ImageNet classification. In this section, we study the importance of noise and the effect of several noise methods used in our model. Learn more. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. 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. Are you sure you want to create this branch? 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. supervised model from 97.9% accuracy to 98.6% accuracy. Imaging, 39 (11) (2020), pp.
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