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. See 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. Parthasarathi et al. 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. 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. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Noisy StudentImageNetEfficientNet-L2state-of-the-art. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. 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. The abundance of data on the internet is vast. We start with the 130M unlabeled images and gradually reduce the number of images. self-mentoring outperforms data augmentation and self training. IEEE Trans. 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. Our main results are shown in Table1. 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. 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. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. Their main goal is to find a small and fast model for deployment. 10687-10698 Abstract 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. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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 As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. task. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. To achieve this result, we first train an EfficientNet model on labeled 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. A tag already exists with the provided branch name. Work fast with our official CLI. unlabeled images. . The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. 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 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. Self-training 1 2Self-training 3 4n What is Noisy Student? We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. 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. Especially unlabeled images are plentiful and can be collected with ease. https://arxiv.org/abs/1911.04252. We iterate this process by putting back the student as the teacher. 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 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. 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. Self-training with Noisy Student improves ImageNet classification Abstract. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. In the following, we will first describe experiment details to achieve our results. It implements SemiSupervised Learning with Noise to create an Image Classification. 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. Please supervised model from 97.9% accuracy to 98.6% accuracy. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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 apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. 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 larger EfficientNet as a student model on the combination of labeled and pseudo 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. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Code for Noisy Student Training. This invariance constraint reduces the degrees of freedom in the model. If nothing happens, download Xcode and try again. 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. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. 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. student is forced to learn harder from the pseudo labels. 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. 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. There was a problem preparing your codespace, please try again. If nothing happens, download Xcode and try again. Are you sure you want to create this branch? 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. 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. We present a simple self-training method that achieves 87.4 This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. The baseline model achieves an accuracy of 83.2. Computer Science - Computer Vision and Pattern Recognition. Learn more. and surprising gains on robustness and adversarial benchmarks. We then train a larger EfficientNet as a student model on the 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. 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 hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. 3429-3440. . Papers With Code is a free resource with all data licensed under. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Yalniz et al. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. Code is available at https://github.com/google-research/noisystudent. On robustness test sets, it improves The architectures for the student and teacher models can be the same or different. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model For classes where we have too many images, we take the images with the highest confidence. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. 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. 3.5B weakly labeled Instagram images. 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. Our procedure went as follows. It is expensive and must be done with great care. 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. Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks.
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