self training with noisy student improves imagenet classification

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. Do better imagenet models transfer better? to use Codespaces. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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 find that Noisy Student is better with an additional trick: data balancing. 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. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 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. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Their main goal is to find a small and fast model for deployment. International Conference on Machine Learning, Learning extraction patterns for subjective expressions, Proceedings of the 2003 conference on Empirical methods in natural language processing, A. Roy Chowdhury, P. Chakrabarty, A. Singh, S. Jin, H. Jiang, L. Cao, and E. G. Learned-Miller, Automatic adaptation of object detectors to new domains using self-training, T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, Probability of error of some adaptive pattern-recognition machines, W. Shi, Y. Gong, C. Ding, Z. MaXiaoyu Tao, and N. Zheng, Transductive semi-supervised deep learning using min-max features, C. Simon-Gabriel, Y. Ollivier, L. Bottou, B. Schlkopf, and D. Lopez-Paz, First-order adversarial vulnerability of neural networks and input dimension, Very deep convolutional networks for large-scale image recognition, N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. Here we study how to effectively use out-of-domain data. We iterate this process by putting back the student as the teacher. We do not tune these hyperparameters extensively since our method is highly robust to them. 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. Self-Training With Noisy Student Improves ImageNet Classification mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. 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 present a simple self-training method that achieves 87.4 Self-Training With Noisy Student Improves ImageNet Classification Self-training with Noisy Student improves ImageNet classification The most interesting image is shown on the right of the first row. Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. We used the version from [47], which filtered the validation set of ImageNet. ImageNet-A top-1 accuracy from 16.6 Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. 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. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. 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 also list EfficientNet-B7 as a reference. In particular, we first perform normal training with a smaller resolution for 350 epochs. 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. 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. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. When data augmentation noise is used, the student must ensure that a translated image, for example, should have the same category with a non-translated image. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. There was a problem preparing your codespace, please try again. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). We determine number of training steps and the learning rate schedule by the batch size for labeled images. Are you sure you want to create this branch? Work fast with our official CLI. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards This is probably because it is harder to overfit the large unlabeled dataset. We sample 1.3M images in confidence intervals. sign in Astrophysical Observatory. (using extra training data). The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. SelfSelf-training with Noisy Student improves ImageNet classification The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Our study shows that using unlabeled data improves accuracy and general robustness. 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. Self-training with Noisy Student. Computer Science - Computer Vision and Pattern Recognition. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. 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. 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. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. Noisy StudentImageNetEfficientNet-L2state-of-the-art. 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. CLIP: Connecting text and images - OpenAI 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 . Self-training with Noisy Student improves ImageNet classification Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality unlabeled images. The width. 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. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. self-mentoring outperforms data augmentation and self training. 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. GitHub - google-research/noisystudent: Code for Noisy Student Training 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. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. During this process, we kept increasing the size of the student model to improve the performance. FixMatch-LS: Semi-supervised skin lesion classification with label They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. The architectures for the student and teacher models can be the same or different. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. 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. The main difference between our work and prior works is that we identify the importance of noise, and aggressively inject noise to make the student better. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. But during the learning of the student, we inject noise such as data As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. A number of studies, e.g. If nothing happens, download Xcode and try again. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). A common workaround is to use entropy minimization or ramp up the consistency loss. However, manually annotating organs from CT scans is time . This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin It is expensive and must be done with great care. 10687-10698). This material is presented to ensure timely dissemination of scholarly and technical work. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. . In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. A tag already exists with the provided branch name. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. We use a resolution of 800x800 in this experiment. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. 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 investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. In other words, small changes in the input image can cause large changes to the predictions. On robustness test sets, it improves Parthasarathi et al. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. We thank the Google Brain team, Zihang Dai, Jeff Dean, Hieu Pham, Colin Raffel, Ilya Sutskever and Mingxing Tan for insightful discussions, Cihang Xie for robustness evaluation, Guokun Lai, Jiquan Ngiam, Jiateng Xie and Adams Wei Yu for feedbacks on the draft, Yanping Huang and Sameer Kumar for improving TPU implementation, Ekin Dogus Cubuk and Barret Zoph for help with RandAugment, Yanan Bao, Zheyun Feng and Daiyi Peng for help with the JFT dataset, Olga Wichrowska and Ola Spyra for help with infrastructure. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Here we study if it is possible to improve performance on small models by using a larger teacher model, since small models are useful when there are constraints for model size and latency in real-world applications. Zoph et al. 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 have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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. Train a larger classifier on the combined set, adding noise (noisy student). (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. 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. 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. 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. If nothing happens, download GitHub Desktop and try again. Ranked #14 on 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. on ImageNet ReaL. Summarization_self-training_with_noisy_student_improves_imagenet We will then show our results on ImageNet and compare them with state-of-the-art models. Then, that teacher is used to label the unlabeled data. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Train a larger classifier on the combined set, adding noise (noisy student). As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative If you get a better model, you can use the model to predict pseudo-labels on the filtered data. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. Our work is based on self-training (e.g.,[59, 79, 56]). In this section, we study the importance of noise and the effect of several noise methods used in our model. Especially unlabeled images are plentiful and can be collected with ease. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. Self-training with Noisy Student improves ImageNet classification Original paper: https://arxiv.org/pdf/1911.04252.pdf Authors: Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le HOYA012 Introduction EfficientNet ImageNet SOTA EfficientNet 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. For classes where we have too many images, we take the images with the highest confidence. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. 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. CVPR 2020 Open Access Repository Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. 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.

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