putting back the student as the teacher. We iterate this process by putting back the student as the teacher. 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. See If nothing happens, download Xcode and try again. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 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. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. Our procedure went as follows. et al. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. 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. Self-training with Noisy Student improves ImageNet classification. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. Noisy Student (EfficientNet) - huggingface.co The results also confirm that vision models can benefit from Noisy Student even without iterative training. We then train a larger EfficientNet as a student model on the Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Do better imagenet models transfer better? On robustness test sets, it improves ImageNet-A top . As shown in Table2, Noisy Student with EfficientNet-L2 achieves 87.4% top-1 accuracy which is significantly better than the best previously reported accuracy on EfficientNet of 85.0%. GitHub - google-research/noisystudent: Code for Noisy Student Training Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. We use a resolution of 800x800 in this experiment. Use Git or checkout with SVN using the web URL. 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. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 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. This is probably because it is harder to overfit the large unlabeled dataset. We iterate this process by putting back the student as the teacher. The architectures for the student and teacher models can be the same or different. Self-training with Noisy Student improves ImageNet 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, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10687-10698, (2020 . 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. To achieve this result, we first train an EfficientNet model on labeled On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Proceedings of the eleventh annual conference on Computational learning theory, Proceedings of the IEEE conference on computer vision and pattern recognition, Empirical Methods in Natural Language Processing (EMNLP), Imagenet classification with deep convolutional neural networks, Domain adaptive transfer learning with specialist models, Thirty-Second AAAI Conference on Artificial Intelligence, Regularized evolution for image classifier architecture search, Inception-v4, inception-resnet and the impact of residual connections on learning. 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 First, a teacher model is trained in a supervised fashion. Infer labels on a much larger unlabeled dataset. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. 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. We duplicate images in classes where there are not enough images. 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 The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. During this process, we kept increasing the size of the student model to improve the performance. We iterate this process by 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. Self-training with Noisy Student improves ImageNet classification. A tag already exists with the provided branch name. 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. sign in Code for Noisy Student Training. Different kinds of noise, however, may have different effects. 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. Please Self-mentoring: : A new deep learning pipeline to train a self 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. 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. This invariance constraint reduces the degrees of freedom in the model. Add a to use Codespaces. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. We sample 1.3M images in confidence intervals. 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]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. The 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. Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. Self-Training With Noisy Student Improves ImageNet Classification We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Astrophysical Observatory. 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. 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. IEEE Transactions on Pattern Analysis and Machine Intelligence. Self-training with Noisy Student improves ImageNet classification Self-training with Noisy Student improves ImageNet classification 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. In particular, we first perform normal training with a smaller resolution for 350 epochs. . on ImageNet, which is 1.0 We improved it by adding noise to the student to learn beyond the teachers knowledge. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. Train a larger classifier on the combined set, adding noise (noisy student). Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and 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]. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. On . sign in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. A common workaround is to use entropy minimization or ramp up the consistency loss. In other words, the student is forced to mimic a more powerful ensemble model. These CVPR 2020 papers are the Open Access versions, provided by the. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Especially unlabeled images are plentiful and can be collected with ease. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). Finally, we iterate the algorithm a few times by treating the student as a teacher to generate new pseudo labels and train a new student. Our main results are shown in Table1. 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. 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. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. 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. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Noisy Student leads to significant improvements across all model sizes for EfficientNet. 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. We will then show our results on ImageNet and compare them with state-of-the-art models. ImageNet . During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. A number of studies, e.g. Are you sure you want to create this branch? 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. Self-training with Noisy Student improves ImageNet classification Train a classifier on labeled data (teacher). As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Notice, Smithsonian Terms of Use Git or checkout with SVN using the web URL. 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. https://arxiv.org/abs/1911.04252. The performance drops when we further reduce it. Different types of. We use the same architecture for the teacher and the student and do not perform iterative training. The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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. Noisy Student can still improve the accuracy to 1.6%. combination of labeled and pseudo labeled images. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. [68, 24, 55, 22]. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. 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. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . 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. Code is available at https://github.com/google-research/noisystudent. 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 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. We also list EfficientNet-B7 as a reference. Soft pseudo labels lead to better performance for low confidence data. Work fast with our official CLI. 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. to use Codespaces. A semi-supervised segmentation network based on noisy student learning The abundance of data on the internet is vast. . Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. For each class, we select at most 130K images that have the highest confidence. 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. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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 algorithm is basically self-training, a method in semi-supervised learning (. 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]. Imaging, 39 (11) (2020), pp. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. Then, that teacher is used to label the unlabeled data. Self-training 1 2Self-training 3 4n What is Noisy Student? corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from student is forced to learn harder from the pseudo labels. 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. 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. Why Self-training with Noisy Students beats SOTA Image classification In other words, small changes in the input image can cause large changes to the predictions. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Models are available at this https URL. CLIP: Connecting text and images - OpenAI In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Noisy StudentImageNetEfficientNet-L2state-of-the-art. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. On, International journal of molecular sciences. Code is available at https://github.com/google-research/noisystudent. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Especially unlabeled images are plentiful and can be collected with ease. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. We then perform data filtering and balancing on this corpus. 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]). We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. For RandAugment, we apply two random operations with the magnitude set to 27. 10687-10698 Abstract 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. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. Le. Self-training with Noisy Student improves ImageNet classification 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. Chowdhury et al. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Abdominal organ segmentation is very important for clinical applications. 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. It implements SemiSupervised Learning with Noise to create an Image Classification. Self-training with Noisy Student - 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 first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. Distillation Survey : Noisy Student | 9to5Tutorial and surprising gains on robustness and adversarial benchmarks. 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. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. We do not tune these hyperparameters extensively since our method is highly robust to them.
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