self training with noisy student improves imagenet classification

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 found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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. Noisy Students performance improves with more unlabeled data. Edit social preview. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. 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. On . In contrast, the predictions of the model with Noisy Student remain quite stable. For instance, on the right column, as the image of the car undergone a small rotation, the standard model changes its prediction from racing car to car wheel to fire engine. 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. 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 Similar to[71], we fix the shallow layers during finetuning. 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. Self-Training With Noisy Student Improves ImageNet Classification. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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. Please In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. [57] used self-training for domain adaptation. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. We then train a larger EfficientNet as a student model on the Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical The results also confirm that vision models can benefit from Noisy Student even without iterative training. We determine number of training steps and the learning rate schedule by the batch size for labeled images. 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. 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. We will then show our results on ImageNet and compare them with state-of-the-art models. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. putting back the student as the teacher. on ImageNet, which is 1.0 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. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. 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. In particular, we first perform normal training with a smaller resolution for 350 epochs. If nothing happens, download Xcode and try again. The most interesting image is shown on the right of the first row. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. supervised model from 97.9% accuracy to 98.6% accuracy. We iterate this process by putting back the student as the teacher. 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 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]). Learn more. We use the labeled images to train a teacher model using the standard cross entropy loss. The baseline model achieves an accuracy of 83.2. Our procedure went as follows. Self-training with noisy student improves imagenet classification. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled 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. 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. 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. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. . [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . Ranked #14 on But training robust supervised learning models is requires this step. We use the same architecture for the teacher and the student and do not perform iterative training. (using extra training data). Image Classification Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This model investigates a new method. There was a problem preparing your codespace, please try again. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Their main goal is to find a small and fast model for deployment. unlabeled images. Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le Description: 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. 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. . Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . We use stochastic depth[29], dropout[63] and RandAugment[14]. 3429-3440. . Code for Noisy Student Training. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. The abundance of data on the internet is vast. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. 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. Astrophysical Observatory. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised 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. We use the standard augmentation instead of RandAugment in this experiment. 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. 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). We present a simple self-training method that achieves 87.4 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. Models are available at this https URL. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. 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. We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. Code for Noisy Student Training. Hence, a question that naturally arises is why the student can outperform the teacher with soft 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. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. 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. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Train a classifier on labeled data (teacher). Please refer to [24] for details about mCE and AlexNets error rate. 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. The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. 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]. 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. 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. 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. 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. Noisy Student Training 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. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Noisy Student leads to significant improvements across all model sizes for EfficientNet. [68, 24, 55, 22]. Here we study how to effectively use out-of-domain data. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. A. Alemi, Thirty-First AAAI Conference on Artificial Intelligence, C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, EfficientNet: rethinking model scaling for convolutional neural networks, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, A. Vedaldi, M. Douze, and H. Jgou, Fixing the train-test resolution discrepancy, V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), J. Weston, F. Ratle, H. Mobahi, and R. Collobert, Deep learning via semi-supervised embedding, Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le, Unsupervised data augmentation for consistency training, S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, I. Yalniz et al. 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.

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