Self-Training with Noisy Student Improves ImageNet Classification 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). The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. This model investigates a new method. Do better imagenet models transfer better? These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. et al. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. On robustness test sets, it improves 10687-10698 Abstract The results also confirm that vision models can benefit from Noisy Student even without iterative training. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. We first report the validation set accuracy on the ImageNet 2012 ILSVRC challenge prediction task as commonly done in literature[35, 66, 23, 69] (see also [55]). 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. 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. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. to use Codespaces. A number of studies, e.g. In terms of methodology, 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. Their noise model is video specific and not relevant for image classification. . 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. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. unlabeled images. 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. to use Codespaces. Here we show the evidence in Table 6, noise such as stochastic depth, dropout and data augmentation plays an important role in enabling the student model to perform better than the teacher. 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. Self-training with Noisy Student improves ImageNet classification Are labels required for improving adversarial robustness? A common workaround is to use entropy minimization or ramp up the consistency loss. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Self-training with Noisy Student improves ImageNet classification We will then show our results on ImageNet and compare them with state-of-the-art models. 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. FixMatch-LS: Semi-supervised skin lesion classification with label Semi-supervised medical image classification with relation-driven self-ensembling model. 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. We then use the teacher model to generate pseudo labels on 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. The performance consistently drops with noise function removed. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Self-training with Noisy Student improves ImageNet classification The width. ImageNet images and use it as a teacher to generate pseudo labels on 300M Self-Training With Noisy Student Improves ImageNet Classification. student is forced to learn harder from the pseudo labels. 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. Noisy StudentImageNetEfficientNet-L2state-of-the-art. For example, without Noisy Student, the model predicts bullfrog for the image shown on the left of the second row, which might be resulted from the black lotus leaf on the water. A tag already exists with the provided branch name. Especially unlabeled images are plentiful and can be collected with ease. sign in Specifically, as all classes in ImageNet have a similar number of labeled images, we also need to balance the number of unlabeled images for each class. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Train a classifier on labeled data (teacher). Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. Self-training with Noisy Student. Add a Our work is based on self-training (e.g.,[59, 79, 56]). IEEE Transactions on Pattern Analysis and Machine Intelligence. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. If nothing happens, download Xcode and try again. , have shown that computer vision models lack robustness. 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. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. With Noisy Student, the model correctly predicts dragonfly for the image. Self-Training Noisy Student " " Self-Training . Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. supervised model from 97.9% accuracy to 98.6% accuracy. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Self-Training for Natural Language Understanding! For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a SelfSelf-training with Noisy Student improves ImageNet classification Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. 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. 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. 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. Please refer to [24] for details about mFR and AlexNets flip probability. The abundance of data on the internet is vast. The results are shown in Figure 4 with the following observations: (1) Soft pseudo labels and hard pseudo labels can both lead to great improvements with in-domain unlabeled images i.e., high-confidence images. Use Git or checkout with SVN using the web URL. Infer labels on a much larger unlabeled dataset. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. combination of labeled and pseudo labeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. There was a problem preparing your codespace, please try again. Models are available at this https URL. 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. Due to duplications, there are only 81M unique images among these 130M images. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. We use a resolution of 800x800 in this experiment. Abdominal organ segmentation is very important for clinical applications. 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 . This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. For simplicity, we experiment with using 1128,164,132,116,14 of the whole data by uniformly sampling images from the the unlabeled set though taking the images with highest confidence leads to better results. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . https://arxiv.org/abs/1911.04252. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Noisy Student can still improve the accuracy to 1.6%. It is expensive and must be done with great care. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. We use the labeled images to train a teacher model using the standard cross entropy loss. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. As can be seen from the figure, our model with Noisy Student makes correct predictions for images under severe corruptions and perturbations such as snow, motion blur and fog, while the model without Noisy Student suffers greatly under these conditions. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We determine number of training steps and the learning rate schedule by the batch size for labeled images. on ImageNet ReaL.

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