As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . Learn more. We present a simple self-training method that achieves 87.4 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. Please refer to [24] for details about mCE and AlexNets error rate. 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 . 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. combination of labeled and pseudo labeled 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We used the version from [47], which filtered the validation set of ImageNet. If nothing happens, download Xcode and try again. 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]. 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. Self-training with Noisy Student improves ImageNet classification 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. Iterative training is not used here for simplicity. This invariance constraint reduces the degrees of freedom in the model. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical 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. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. Self-training with Noisy Student improves ImageNet classification 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. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . w Summary of key results compared to previous state-of-the-art models. 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. We start with the 130M unlabeled images and gradually reduce the number of images. However an important requirement for Noisy Student to work well is that the student model needs to be sufficiently large to fit more data (labeled and pseudo labeled). on ImageNet, which is 1.0 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. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 3.5B weakly labeled Instagram images. On robustness test sets, it improves 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. We also list EfficientNet-B7 as a reference. 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. But during the learning of the student, we inject noise such as data et al. Self-Training with Noisy Student Improves ImageNet Classification 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. - : self-training_with_noisy_student_improves_imagenet_classification FixMatch-LS: Semi-supervised skin lesion classification with label If you get a better model, you can use the model to predict pseudo-labels on the filtered data. putting back the student as the teacher. 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. 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. Test images on ImageNet-P underwent different scales of perturbations. Astrophysical Observatory. (or is it just me), Smithsonian Privacy 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. 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. 27.8 to 16.1. Ranked #14 on In contrast, the predictions of the model with Noisy Student remain quite stable. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Self-training with Noisy Student improves ImageNet classification Code is available at https://github.com/google-research/noisystudent. The architectures for the student and teacher models can be the same or different. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Self-training with Noisy Student - IEEE Trans. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. 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. During this process, we kept increasing the size of the student model to improve the performance. Self-training In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher 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. With Noisy Student, the model correctly predicts dragonfly for the image. The ONCE (One millioN sCenEs) dataset for 3D object detection in the autonomous driving scenario is introduced and a benchmark is provided in which a variety of self-supervised and semi- supervised methods on the ONCE dataset are evaluated. 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. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Noise Self-training with Noisy Student 1. For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. For example, without Noisy Student, the model predicts bullfrog for the image shown on the left of the second row, which might be resulted from the black lotus leaf on the water. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. By showing the models only labeled images, we limit ourselves from making use of unlabeled images available in much larger quantities to improve accuracy and robustness of state-of-the-art models. 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. 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. 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. all 12, Image Classification Self-training with Noisy Student improves ImageNet classification Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. [57] used self-training for domain adaptation. supervised model from 97.9% accuracy to 98.6% accuracy. 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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 Improves ImageNet Classification. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Edit social preview. 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. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. Use, Smithsonian 2023.3.1_2 - We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Our work is based on self-training (e.g.,[59, 79, 56]). 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. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 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. Papers With Code is a free resource with all data licensed under. 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. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. In this section, we study the importance of noise and the effect of several noise methods used in our model. 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. Infer labels on a much larger unlabeled dataset. There was a problem preparing your codespace, please try again. We use the standard augmentation instead of RandAugment in this experiment. A tag already exists with the provided branch name. 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. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. See The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. Do imagenet classifiers generalize to imagenet? The best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. 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. The main use case of knowledge distillation is model compression by making the student model smaller. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Use Git or checkout with SVN using the web URL. 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-training with Noisy Student improves ImageNet classication Qizhe Xie 1, Minh-Thang Luong , Eduard Hovy2, Quoc V. Le1 1Google Research, Brain Team, 2Carnegie Mellon University fqizhex, thangluong, qvlg@google.com, hovy@cmu.edu Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Train a larger classifier on the combined set, adding noise (noisy student). A. Krizhevsky, I. Sutskever, and G. E. Hinton, Temporal ensembling for semi-supervised learning, Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks, Workshop on Challenges in Representation Learning, ICML, Certainty-driven consistency loss for semi-supervised learning, C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, R. G. Lopes, D. Yin, B. Poole, J. Gilmer, and E. D. 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