self training with noisy student improves imagenet classification

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. unlabeled images , . 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Parthasarathi et al. [68, 24, 55, 22]. 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. E. Arazo, D. Ortego, P. Albert, N. E. OConnor, and K. 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This material is presented to ensure timely dissemination of scholarly and technical work. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. Please 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. 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Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. 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. It implements SemiSupervised Learning with Noise to create an Image Classification. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. But during the learning of the student, we inject noise such as data On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. Med. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. We sample 1.3M images in confidence intervals. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Code for Noisy Student Training. 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. over the JFT dataset to predict a label for each image. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. We use a resolution of 800x800 in this experiment. Specifically, as all classes in ImageNet have a similar number of labeled images, we also need to balance the number of unlabeled images for each class. 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. 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. 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. These significant gains in robustness in ImageNet-C and ImageNet-P are surprising because our models were not deliberately optimizing for robustness (e.g., via data augmentation). First, we run an EfficientNet-B0 trained on ImageNet[69]. On . 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. We apply dropout to the final classification layer with a dropout rate of 0.5. 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]. 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. 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. We use stochastic depth[29], dropout[63] and RandAugment[14]. 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. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. In other words, small changes in the input image can cause large changes to the predictions. We do not tune these hyperparameters extensively since our method is highly robust to them. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. self-mentoring outperforms data augmentation and self training. 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. Chowdhury et al. 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. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 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 then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. 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. 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. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. ImageNet images and use it as a teacher to generate pseudo labels on 300M . Abdominal organ segmentation is very important for clinical applications. If nothing happens, download GitHub Desktop and try again. 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. 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. 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. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. et al. Imaging, 39 (11) (2020), pp. 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. The main use case of knowledge distillation is model compression by making the student model smaller. sign in Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior.