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. 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. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 Code for Noisy Student Training. 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. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. If nothing happens, download GitHub Desktop and try again. We iterate this process by putting back the student as 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 : Noisy Student : Zoph et al. The most interesting image is shown on the right of the first row. On robustness test sets, it improves 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. 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. 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. 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. 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. IEEE Trans. We find that Noisy Student is better with an additional trick: data balancing. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. 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. Are labels required for improving adversarial robustness? We duplicate images in classes where there are not enough images. We also list EfficientNet-B7 as a reference. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Here we study how to effectively use out-of-domain data. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. possible. 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. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. PDF Self-Training with Noisy Student Improves ImageNet Classification 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. Work fast with our official CLI. Chowdhury et al. . The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Self-training with Noisy Student improves ImageNet classification combination of labeled and pseudo labeled images. Noisy Student Training is a semi-supervised learning approach. 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. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. See 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 robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. FixMatch-LS: Semi-supervised skin lesion classification with label The architectures for the student and teacher models can be the same or different. We determine number of training steps and the learning rate schedule by the batch size for labeled images. 10687-10698). mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. . The inputs to the algorithm are both labeled and unlabeled images. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. First, we run an EfficientNet-B0 trained on ImageNet[69]. However, manually annotating organs from CT scans is time . 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. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. [57] used self-training for domain adaptation. 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. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Different types of. For RandAugment, we apply two random operations with the magnitude set to 27. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: For ImageNet checkpoints trained by Noisy Student Training, please refer to the EfficientNet github. Imaging, 39 (11) (2020), pp. CLIP: Connecting text and images - OpenAI 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. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. 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. We use stochastic depth[29], dropout[63] and RandAugment[14]. all 12, Image Classification 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 terms of methodology, Self-Training With Noisy Student Improves ImageNet Classification Train a larger classifier on the combined set, adding noise (noisy student). Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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Self-training is a form of semi-supervised learning [10] which attempts to leverage unlabeled data to improve classification performance in the limited data regime. Their noise model is video specific and not relevant for image 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 train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. Copyright and all rights therein are retained by authors or by other copyright holders. Code is available at https://github.com/google-research/noisystudent. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Self-Training With Noisy Student Improves ImageNet Classification We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Add a 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 other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. supervised model from 97.9% accuracy to 98.6% accuracy. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. 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. 2023.3.1_2 - Self-training with Noisy Student improves ImageNet classification Self-training with Noisy Student improves ImageNet classification. Self-training with Noisy Student improves ImageNet classification Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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. (using extra training data). Are you sure you want to create this branch? 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. If nothing happens, download Xcode and try again. 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. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Semi-supervised medical image classification with relation-driven self-ensembling model. . Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. 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. We do not tune these hyperparameters extensively since our method is highly robust to them. 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.
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