However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Self-Training With Noisy Student Improves ImageNet Classification 10687-10698 Abstract 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. Iterative training is not used here for simplicity. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model sign in Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. We iterate this process by We find that using a batch size of 512, 1024, and 2048 leads to the same performance. Ranked #14 on Self-mentoring: : A new deep learning pipeline to train a self Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 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. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Different kinds of noise, however, may have different effects. 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. For instance, on ImageNet-A, Noisy Student achieves 74.2% top-1 accuracy which is approximately 57% more accurate than the previous state-of-the-art model. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. . In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. 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 particular, we set the survival probability in stochastic depth to 0.8 for the final layer and follow the linear decay rule for other layers. task. During the generation of the pseudo 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Finally, in the above, we say that the pseudo labels can be soft or hard. But during the learning of the student, we inject noise such as data 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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%. Astrophysical Observatory. 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. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Noisy Student (EfficientNet) - huggingface.co For example, with all noise removed, the accuracy drops from 84.9% to 84.3% in the case with 130M unlabeled images and drops from 83.9% to 83.2% in the case with 1.3M unlabeled images. However, in the case with 130M unlabeled images, with noise function removed, the performance is still improved to 84.3% from 84.0% when compared to the supervised baseline. 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. This model investigates a new method. 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. We iterate this process by putting back the student as the teacher. It can be seen that masks are useful in improving classification performance. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. The main use case of knowledge distillation is model compression by making the student model smaller. We use the same architecture for the teacher and the student and do not perform iterative 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. 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. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. The abundance of data on the internet is vast. Train a larger classifier on the combined set, adding noise (noisy student). 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. 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 evaluate the best model, that achieves 87.4% top-1 accuracy, on three robustness test sets: ImageNet-A, ImageNet-C and ImageNet-P. ImageNet-C and P test sets[24] include images with common corruptions and perturbations such as blurring, fogging, rotation and scaling. over the JFT dataset to predict a label for each image. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. Then, that teacher is used to label the unlabeled data. 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. 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. First, a teacher model is trained in a supervised fashion. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 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. Self-training with Noisy Student improves ImageNet classification. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Self-Training With Noisy Student Improves ImageNet Classification [^reference-9] [^reference-10] A critical insight was to . - : self-training_with_noisy_student_improves_imagenet_classification The main difference between our method and knowledge distillation is that knowledge distillation does not consider unlabeled data and does not aim to improve the student model. 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. In terms of methodology, Self-training with Noisy Student improves ImageNet classification. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a In other words, the student is forced to mimic a more powerful ensemble model. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin In particular, we first perform normal training with a smaller resolution for 350 epochs. Self-training with Noisy Student. Self-training with Noisy Student improves ImageNet classification You signed in with another tab or window. 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. 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. Self-training with Noisy Student improves ImageNet classification (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. unlabeled images , . We iterate this process by putting back the student as the teacher. on ImageNet ReaL. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. 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. If nothing happens, download GitHub Desktop and try again. Code for Noisy Student Training. Our study shows that using unlabeled data improves accuracy and general robustness. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. Agreement NNX16AC86A, Is ADS down? C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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. Noisy Student Explained | Papers With Code For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. Add a Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Infer labels on a much larger unlabeled dataset. We also study the effects of using different amounts of unlabeled data. Learn more. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Specifically, we train the student model for 350 epochs for models larger than EfficientNet-B4, including EfficientNet-L0, L1 and L2 and train the student model for 700 epochs for smaller models. 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. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial 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. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. 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. 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. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and E. Arazo, D. Ortego, P. Albert, N. E. OConnor, and K. McGuinness, Pseudo-labeling and confirmation bias in deep semi-supervised learning, B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson, There are many consistent explanations of unlabeled data: why you should average, International Conference on Learning Representations, Advances in Neural Information Processing Systems, D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, MixMatch: a holistic approach to semi-supervised learning, Combining labeled and unlabeled data with co-training, C. Bucilu, R. Caruana, and A. Niculescu-Mizil, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Y. Carmon, A. Raghunathan, L. Schmidt, P. Liang, and J. C. Duchi, Unlabeled data improves adversarial robustness, Semi-supervised learning (chapelle, o. et al., eds. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. Image Classification A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. Self-training with Noisy Student improves ImageNet classification However, manually annotating organs from CT scans is time . A common workaround is to use entropy minimization or ramp up the consistency loss. . Their main goal is to find a small and fast model for deployment. 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. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. 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. Self-training with Noisy Student improves ImageNet classificationCVPR2020, Codehttps://github.com/google-research/noisystudent, Self-training, 1, 2Self-training, Self-trainingGoogleNoisy Student, Noisy Studentstudent modeldropout, stochastic depth andaugmentationteacher modelNoisy Noisy Student, Noisy Student, 1, JFT3ImageNetEfficientNet-B00.3130K130K, EfficientNetbaseline modelsEfficientNetresnet, EfficientNet-B7EfficientNet-L0L1L2, batchsize = 2048 51210242048EfficientNet-B4EfficientNet-L0l1L2350epoch700epoch, 2EfficientNet-B7EfficientNet-L0, 3EfficientNet-L0EfficientNet-L1L0, 4EfficientNet-L1EfficientNet-L2, student modelNoisy, noisystudent modelteacher modelNoisy, Noisy, Self-trainingaugmentationdropoutstochastic depth, Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores., 12/self-training-with-noisy-student-f33640edbab2, EfficientNet-L0EfficientNet-B7B7, EfficientNet-L1EfficientNet-L0, EfficientNetsEfficientNet-L1EfficientNet-L2EfficientNet-L2EfficientNet-B75. 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. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 2023.3.1_2 - We find that Noisy Student is better with an additional trick: data balancing. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. ImageNet images and use it as a teacher to generate pseudo labels on 300M Noise Self-training with Noisy Student 1. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. The comparison is shown in Table 9. . 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. 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. CLIP: Connecting text and images - OpenAI Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled 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 each class, we select at most 130K images that have the highest confidence. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. The abundance of data on the internet is vast. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Do imagenet classifiers generalize to imagenet? Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Learn more. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Our main results are shown in Table1. Self-Training for Natural Language Understanding! Distillation Survey : Noisy Student | 9to5Tutorial Le. While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. Self-Training With Noisy Student Improves ImageNet Classification. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet Models are available at this https URL. 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. The performance drops when we further reduce it. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. 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. 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. 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. 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. 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. CVPR 2020 Open Access Repository https://arxiv.org/abs/1911.04252. putting back the student as the teacher. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Self-training with Noisy Student improves ImageNet classification These CVPR 2020 papers are the Open Access versions, provided by the. 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 . It is expensive and must be done with great care. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The results also confirm that vision models can benefit from Noisy Student even without iterative training. 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. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Self-training with Noisy Student improves ImageNet classification. Abdominal organ segmentation is very important for clinical applications. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). We start with the 130M unlabeled images and gradually reduce the number of images. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. 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. Self-training The mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. et al. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. student is forced to learn harder from the pseudo labels. In other words, small changes in the input image can cause large changes to the predictions. Self-training with Noisy Student - On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Self-Training With Noisy Student Improves ImageNet Classification For more information about the large architectures, please refer to Table7 in Appendix A.1. to use Codespaces. Here we use unlabeled images to improve the state-of-the-art ImageNet accuracy and show that the accuracy gain has an outsized impact on robustness. It implements SemiSupervised Learning with Noise to create an Image Classification. 3.5B weakly labeled Instagram images. This is probably because it is harder to overfit the large unlabeled dataset. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. , have shown that computer vision models lack robustness. (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. 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 1 2Self-training 3 4n What is Noisy Student? We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. If nothing happens, download Xcode and try again. 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.
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