corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 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. These CVPR 2020 papers are the Open Access versions, provided by the. It implements SemiSupervised Learning with Noise to create an Image Classification. We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. Self-training with Noisy Student improves ImageNet classification. Notice, Smithsonian Terms of Soft pseudo labels lead to better performance for low confidence data. The baseline model achieves an accuracy of 83.2. In other words, small changes in the input image can cause large changes to the predictions. 10687-10698 Abstract 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. 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. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). 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. Use Git or checkout with SVN using the web URL. The performance consistently drops with noise function removed. Infer labels on a much larger unlabeled dataset. There was a problem preparing your codespace, please try again. Compared to consistency training[45, 5, 74], the self-training / teacher-student framework is better suited for ImageNet because we can train a good teacher on ImageNet using label 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. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. (Submitted on 11 Nov 2019) We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. 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 can still improve the accuracy to 1.6%. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. Astrophysical Observatory. We iterate this process by putting back the student as 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. We start with the 130M unlabeled images and gradually reduce the number of images. This material is presented to ensure timely dissemination of scholarly and technical work. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. 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. 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 By clicking accept or continuing to use the site, you agree to the terms outlined in our. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. on ImageNet ReaL We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. combination of labeled and pseudo labeled images. In particular, we first perform normal training with a smaller resolution for 350 epochs. Due to duplications, there are only 81M unique images among these 130M images. Yalniz et al. 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. A number of studies, e.g. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. 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. - : self-training_with_noisy_student_improves_imagenet_classification unlabeled images , . 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. task. On robustness test sets, it improves The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Our experiments show that an important element for this simple method to work well at scale is that the student model should be noised during its training while the teacher should not be noised during the generation of pseudo labels. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Le. [^reference-9] [^reference-10] A critical insight was to . 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. We also list EfficientNet-B7 as a reference. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. Self-training 1 2Self-training 3 4n What is Noisy Student? In terms of methodology, 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 then perform data filtering and balancing on this corpus. Code for Noisy Student Training. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Work fast with our official CLI. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. A tag already exists with the provided branch name. Abdominal organ segmentation is very important for clinical applications. 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 obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 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. sign in Hence we use soft pseudo labels for our experiments unless otherwise specified. Self-training with Noisy Student improves ImageNet classification Abstract. 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. We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled images. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. 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. Especially unlabeled images are plentiful and can be collected with ease. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram 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. We present a simple self-training method that achieves 87.4 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. 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. Self-training with noisy student improves imagenet classification. 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. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. 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. 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. 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. w Summary of key results compared to previous state-of-the-art models. 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. 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. On, International journal of molecular sciences. 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. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. 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. 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. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. 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. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. 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. Different types of. Learn more. Image Classification Self-training with Noisy Student. 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]). 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. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. 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. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and 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. If nothing happens, download Xcode and try again. A. Alemi, Thirty-First AAAI Conference on Artificial Intelligence, C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, EfficientNet: rethinking model scaling for convolutional neural networks, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, A. Vedaldi, M. Douze, and H. Jgou, Fixing the train-test resolution discrepancy, V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), J. Weston, F. Ratle, H. Mobahi, and R. Collobert, Deep learning via semi-supervised embedding, Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le, Unsupervised data augmentation for consistency training, S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, I. 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. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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. supervised model from 97.9% accuracy to 98.6% accuracy. 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 . As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Self-training Noise Self-training with Noisy Student 1. Our procedure went as follows. During this process, we kept increasing the size of the student model to improve the performance. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 3.5B weakly labeled Instagram images. ImageNet-A top-1 accuracy from 16.6 Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. 3429-3440. . 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. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. You signed in with another tab or window. 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. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. Infer labels on a much larger unlabeled dataset. Noisy Student leads to significant improvements across all model sizes for EfficientNet. over the JFT dataset to predict a label for each image. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Their purpose is different from ours: to adapt a teacher model on one domain to another. 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. 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. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. Similar to[71], we fix the shallow layers during finetuning. The abundance of data on the internet is vast. Computer Science - Computer Vision and Pattern Recognition. 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. There was a problem preparing your codespace, please try again. First, a teacher model is trained in a supervised fashion. This invariance constraint reduces the degrees of freedom in the model. Their main goal is to find a small and fast model for deployment. Next, a larger student model is trained on the combination of all data and achieves better performance than the teacher by itself.OUTLINE:0:00 - Intro \u0026 Overview1:05 - Semi-Supervised \u0026 Transfer Learning5:45 - Self-Training \u0026 Knowledge Distillation10:00 - Noisy Student Algorithm Overview20:20 - Noise Methods22:30 - Dataset Balancing25:20 - Results30:15 - Perturbation Robustness34:35 - Ablation Studies39:30 - Conclusion \u0026 CommentsPaper: https://arxiv.org/abs/1911.04252Code: https://github.com/google-research/noisystudentModels: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnetAbstract:We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. After using the masks generated by teacher-SN, the classification performance improved by 0.2 of AC, 1.2 of SP, and 0.7 of AUC. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. For classes where we have too many images, we take the images with the highest confidence. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). 27.8 to 16.1. For more information about the large architectures, please refer to Table7 in Appendix A.1. 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. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. In this section, we study the importance of noise and the effect of several noise methods used in our model. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 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. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. Models are available at this https URL. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. 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. We use the standard augmentation instead of RandAugment in this experiment. Our work is based on self-training (e.g.,[59, 79, 56]). However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. ImageNet . . In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. 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. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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). We improved it by adding noise to the student to learn beyond the teachers knowledge. . 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. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. Train a classifier on labeled data (teacher). The width. When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single model. Here we study how to effectively use out-of-domain data. 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. putting back the student as the teacher. 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.
Discuss The Stage Of Development Of The Tropical Cyclone Nivar, Scorpio Horoscope Tomorrow, Articles S