self training with noisy student improves imagenet classification

The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. 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. Their noise model is video specific and not relevant for image classification. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. If nothing happens, download Xcode and try again. Noisy Student Training is a semi-supervised learning approach. 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.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. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Please refer to [24] for details about mFR and AlexNets flip probability. 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. 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. Finally, in the above, we say that the pseudo labels can be soft or hard. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. But during the learning of the student, we inject noise such as data Agreement NNX16AC86A, Is ADS down? putting back the student as the teacher. student is forced to learn harder from the pseudo labels. 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. Self-training with Noisy Student improves ImageNet classification. Especially unlabeled images are plentiful and can be collected with ease. Train a larger classifier on the combined set, adding noise (noisy student). We find that using a batch size of 512, 1024, and 2048 leads to the same performance. Learn more. IEEE Trans. to use Codespaces. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. 3.5B weakly labeled Instagram images. Our work is based on self-training (e.g.,[59, 79, 56]). Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . You signed in with another tab or window. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. 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. The baseline model achieves an accuracy of 83.2. 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. 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. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 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]). Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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Add a 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. task. The abundance of data on the internet is vast. To achieve this result, we first train an EfficientNet model on labeled Image Classification 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. You signed in with another tab or window. 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. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. Noisy Student self-training is an effective way to leverage unlabelled datasets and improving accuracy by adding noise to the student model while training so it learns beyond the teacher's knowledge. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. 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. 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. ImageNet images and use it as a teacher to generate pseudo labels on 300M During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Summarization_self-training_with_noisy_student_improves_imagenet On robustness test sets, it improves ImageNet-A top . (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. (using extra training data). 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. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. Parthasarathi et al. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Abdominal organ segmentation is very important for clinical applications. 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. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin Self-training Noisy Student Training is a semi-supervised learning method which achieves 88.4% top-1 accuracy on ImageNet (SOTA) and surprising gains on robustness and adversarial benchmarks. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet unlabeled images. Test images on ImageNet-P underwent different scales of perturbations. Noisy Student Training is a semi-supervised learning method which achieves 88.4% top-1 accuracy on ImageNet (SOTA) and surprising gains on robustness and adversarial benchmarks. We iterate this process by putting back the student as the teacher. Work fast with our official CLI. In the following, we will first describe experiment details to achieve our results. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. 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. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. We also study the effects of using different amounts of unlabeled data. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 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. Self-training with Noisy Student. Papers With Code is a free resource with all data licensed under. Similar to[71], we fix the shallow layers during finetuning. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Code for Noisy Student Training. We iterate this process by If nothing happens, download GitHub Desktop and try again. 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. The performance drops when we further reduce it. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical labels, the teacher is not noised so that the pseudo labels are as good as 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. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 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 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. ImageNet . 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Self-training with Noisy Student improves ImageNet classification Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. 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. This is probably because it is harder to overfit the large unlabeled dataset. 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. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . possible. 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. 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 Are you sure you want to create this branch? Then, that teacher is used to label the unlabeled data. The performance consistently drops with noise function removed. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. We duplicate images in classes where there are not enough images. 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. 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 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 It can be seen that masks are useful in improving classification performance. Noisy Student (EfficientNet) - huggingface.co Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Significantly, after using the masks generated by student-SN, the classification performance improved by 0.9 of AC, 0.7 of SE, and 0.9 of AUC. Self-training with noisy student improves imagenet classification. 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. 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. Train a larger classifier on the combined set, adding noise (noisy student). The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. PDF Self-Training with Noisy Student Improves ImageNet Classification 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. We then use the teacher model to generate pseudo labels on unlabeled images. We iterate this process by putting back the student as the teacher. For classes where we have too many images, we take the images with the highest confidence. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. and surprising gains on robustness and adversarial benchmarks. . This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. We start with the 130M unlabeled images and gradually reduce the number of images. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. combination of labeled and pseudo labeled images. Different kinds of noise, however, may have different effects. https://arxiv.org/abs/1911.04252, Accompanying notebook and sources to "A Guide to Pseudolabelling: How to get a Kaggle medal with only one model" (Dec. 2020 PyData Boston-Cambridge Keynote), Deep learning has shown remarkable successes in image recognition in recent years[35, 66, 62, 23, 69]. This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. 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]. 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. 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. 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 do not tune these hyperparameters extensively since our method is highly robust to them. Use Git or checkout with SVN using the web URL. This work proposes a novel architectural unit, which is term the Squeeze-and-Excitation (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and shows that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. Yalniz et al. Our procedure went as follows. Flip probability is the probability that the model changes top-1 prediction for different perturbations. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. on ImageNet ReaL Figure 1(b) shows images from ImageNet-C and the corresponding predictions. ImageNet-A top-1 accuracy from 16.6 They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. Self-Training With Noisy Student Improves ImageNet Classification Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. For each class, we select at most 130K images that have the highest confidence. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. 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. Noisy Student leads to significant improvements across all model sizes for EfficientNet. 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-mentoring outperforms data augmentation and self training. 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 apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. 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. all 12, Image Classification 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. A semi-supervised segmentation network based on noisy student learning 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. 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. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. 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. As noise injection methods are not used in the student model, and the student model was also small, it is more difficult to make the student better than teacher. For more information about the large architectures, please refer to Table7 in Appendix A.1. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. 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. 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]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Our main results are shown in Table1. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). 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. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. It is expensive and must be done with great care. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Infer labels on a much larger unlabeled dataset. 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. Our study shows that using unlabeled data improves accuracy and general robustness. 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. Self-training with Noisy Student improves ImageNet classification

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