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. Different types of. Self-training with Noisy Student - IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Train a classifier on labeled data (teacher). . 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 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. First, a teacher model is trained in a supervised fashion. 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. We iterate this process by putting back the student as the teacher. A tag already exists with the provided branch name. over the JFT dataset to predict a label for each image. Self-training Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Then, that teacher is used to label the unlabeled data. We then train a larger EfficientNet as a student model on the With Noisy Student, the model correctly predicts dragonfly for the image. 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. We iterate this process by putting back the student as the teacher. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. 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. In other words, the student is forced to mimic a more powerful ensemble model. Noisy Student Explained | Papers With Code Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin Note that these adversarial robustness results are not directly comparable to prior works since we use a large input resolution of 800x800 and adversarial vulnerability can scale with the input dimension[17, 20, 19, 61]. On robustness test sets, it improves ImageNet-A top . Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. 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. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. Do better imagenet models transfer better? We sample 1.3M images in confidence intervals. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. 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. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. Self-training with Noisy Student improves ImageNet classification The proposed use of distillation to only handle easy instances allows for a more aggressive trade-off in the student size, thereby reducing the amortized cost of inference and achieving better accuracy than standard distillation. The width. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. 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 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. It is found that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. 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). arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 Noise Self-training with Noisy Student 1. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. 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. 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 We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. 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. putting back the student as the teacher. task. If nothing happens, download Xcode and try again. 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. Noisy StudentImageNetEfficientNet-L2state-of-the-art. In typical self-training with the teacher-student framework, noise injection to the student is not used by default, or the role of noise is not fully understood or justified. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. . Edit social preview. Self-training with noisy student improves imagenet classification. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. A semi-supervised segmentation network based on noisy student learning Noisy Student (B7, L2) means to use EfficientNet-B7 as the student and use our best model with 87.4% accuracy as the teacher model. 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. For classes where we have too many images, we take the images with the highest confidence. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. Work fast with our official CLI. 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. In the following, we will first describe experiment details to achieve our results. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. 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. 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. 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. Although they have produced promising results, in our preliminary experiments, consistency regularization works less well on ImageNet because consistency regularization in the early phase of ImageNet training regularizes the model towards high entropy predictions, and prevents it from achieving good accuracy. 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. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. Please Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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. Le. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. We determine number of training steps and the learning rate schedule by the batch size for labeled images. (or is it just me), Smithsonian Privacy Self-mentoring: : A new deep learning pipeline to train a self This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Flip probability is the probability that the model changes top-1 prediction for different perturbations. Infer labels on a much larger unlabeled dataset. 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. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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. 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. GitHub - google-research/noisystudent: Code for Noisy Student Training If you get a better model, you can use the model to predict pseudo-labels on the filtered data. ImageNet . Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. First, we run an EfficientNet-B0 trained on ImageNet[69]. Self-training with Noisy Student improves ImageNet classification The main use case of knowledge distillation is model compression by making the student model smaller. Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le Description: 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. This model investigates a new method. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. 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. The algorithm is basically self-training, a method in semi-supervised learning (. 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. 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. 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]). Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. But during the learning of the student, we inject noise such as 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. (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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. to use Codespaces. 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. We use EfficientNet-B4 as both the teacher and the student. In particular, we first perform normal training with a smaller resolution for 350 epochs. However an important requirement for Noisy Student to work well is that the student model needs to be sufficiently large to fit more data (labeled and pseudo labeled). Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Work fast with our official CLI. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Self-Training With Noisy Student Improves ImageNet Classification It implements SemiSupervised Learning with Noise to create an Image Classification. 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. Please refer to [24] for details about mFR and AlexNets flip probability. 10687-10698 Abstract Here we study how to effectively use out-of-domain data. 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. 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. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. 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. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. 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. 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 . However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Our study shows that using unlabeled data improves accuracy and general robustness. IEEE Trans. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative We duplicate images in classes where there are not enough images. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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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. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Self-Training With Noisy Student Improves ImageNet Classification We iterate this process by putting back the student as the teacher. Self-Training for Natural Language Understanding!