Self-training with Noisy Student improves ImageNet classification 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. Train a classifier on labeled data (teacher). Our study shows that using unlabeled data improves accuracy and general robustness. We iterate this process by combination of labeled and pseudo labeled images. Self-training with Noisy Student improves ImageNet classification In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. CLIP (Contrastive Language-Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.The idea of zero-data learning dates back over a decade [^reference-8] but until recently was mostly studied in computer vision as a way of generalizing to unseen object categories. Noisy Student Training seeks to improve on self-training and distillation in two ways. 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. 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. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . 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. on ImageNet ReaL First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. 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. et al. On, International journal of molecular sciences. Summarization_self-training_with_noisy_student_improves_imagenet_classification. During this process, we kept increasing the size of the student model to improve the performance. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative on ImageNet ReaL. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Abdominal organ segmentation is very important for clinical applications. Due to duplications, there are only 81M unique images among these 130M images. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Notice, Smithsonian Terms of An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. With Noisy Student, the model correctly predicts dragonfly for the image. The accuracy is improved by about 10% in most settings. To achieve strong results on ImageNet, the student model also needs to be large, typically larger than common vision models, so that it can leverage a large number of 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. 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. Edit social preview. ImageNet images and use it as a teacher to generate pseudo labels on 300M We start with the 130M unlabeled images and gradually reduce the number of images. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. 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 also study the effects of using different amounts of unlabeled data. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. We iterate this process by putting back the student as the teacher. 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. 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. We evaluate the best model, that achieves 87.4% top-1 accuracy, on three robustness test sets: ImageNet-A, ImageNet-C and ImageNet-P. ImageNet-C and P test sets[24] include images with common corruptions and perturbations such as blurring, fogging, rotation and scaling. [57] used self-training for domain adaptation. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . 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. Since we use soft pseudo labels generated from the teacher model, when the student is trained to be exactly the same as the teacher model, the cross entropy loss on unlabeled data would be zero and the training signal would vanish. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. Chowdhury et al. 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. 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 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. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. 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. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. Self-training with Noisy Student improves ImageNet classification For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. 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. On robustness test sets, it improves 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. Not only our method improves standard ImageNet accuracy, it also improves classification robustness on much harder test sets by large margins: ImageNet-A[25] top-1 accuracy from 16.6% to 74.2%, ImageNet-C[24] mean corruption error (mCE) from 45.7 to 31.2 and ImageNet-P[24] mean flip rate (mFR) from 27.8 to 16.1. 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. Le. 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]. We do not tune these hyperparameters extensively since our method is highly robust to them. [68, 24, 55, 22]. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 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. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . labels, the teacher is not noised so that the pseudo labels are as good as For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards Self-training with Noisy Student - Medium 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. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16].
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