self training with noisy student improves imagenet classification

Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. In other words, the student is forced to mimic a more powerful ensemble model. Finally, in the above, we say that the pseudo labels can be soft or hard. ImageNet-A top-1 accuracy from 16.6 - : self-training_with_noisy_student_improves_imagenet_classification In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. GitHub - google-research/noisystudent: 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. 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. It 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. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. First, a teacher model is trained in a supervised fashion. 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. SelfSelf-training with Noisy Student improves ImageNet classification There was a problem preparing your codespace, please try again. 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. The learning rate starts at 0.128 for labeled batch size 2048 and decays by 0.97 every 2.4 epochs if trained for 350 epochs or every 4.8 epochs if trained for 700 epochs. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. 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. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model tsai - Noisy student Self-training with Noisy Student improves ImageNet classification 3.5B weakly labeled Instagram images. Noisy Student leads to significant improvements across all model sizes for EfficientNet. 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 Noisy Student " " Self-Training . unlabeled images , . Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. We duplicate images in classes where there are not enough images. 2023.3.1_2 - CLIP: Connecting text and images - OpenAI Figure 1(c) shows images from ImageNet-P and the corresponding predictions. Code is available at https://github.com/google-research/noisystudent. possible. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y. Computer Science - Computer Vision and Pattern 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. Do better imagenet models transfer better? 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. The performance drops when we further reduce it. Please We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Train a classifier on labeled data (teacher). You signed in with another tab or window. . and surprising gains on robustness and adversarial benchmarks. 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. Noisy Student Training is a semi-supervised learning approach. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Infer labels on a much larger unlabeled dataset. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Code for Noisy Student Training. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. Self-training with Noisy Student - Medium Test images on ImageNet-P underwent different scales of perturbations. If nothing happens, download GitHub Desktop and try again. But training robust supervised learning models is requires this step. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. However, manually annotating organs from CT scans is time . Self-training with Noisy Student improves ImageNet classification Abstract. 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. 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. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. 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]). 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. Summarization_self-training_with_noisy_student_improves_imagenet You signed in with another tab or window. Please refer to [24] for details about mFR and AlexNets flip probability. On robustness test sets, it improves ImageNet-A top . In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. Self-training 1 2Self-training 3 4n What is Noisy Student? 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. Use Git or checkout with SVN using the web URL. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. Self-training with Noisy Student improves ImageNet classification. 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 find that using a batch size of 512, 1024, and 2048 leads to the same performance. We also list EfficientNet-B7 as a reference. The architectures for the student and teacher models can be the same or different. Soft pseudo labels lead to better performance for low confidence data. 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]. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . 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]. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. sign in By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. Image Classification 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. We iterate this process by putting back the student as the teacher. 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. Our main results are shown in Table1. Different kinds of noise, however, may have different effects. Not only our method improves standard ImageNet accuracy, it also improves classification robustness on much harder test sets by large margins: ImageNet-A[25] top-1 accuracy from 16.6% to 74.2%, ImageNet-C[24] mean corruption error (mCE) from 45.7 to 31.2 and ImageNet-P[24] mean flip rate (mFR) from 27.8 to 16.1. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. Self-Training for Natural Language Understanding! In terms of methodology, Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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 first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Self-training with Noisy Student improves ImageNet classification As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. During the generation of the pseudo 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. Infer labels on a much larger unlabeled dataset. Iterative training is not used here for simplicity. It implements SemiSupervised Learning with Noise to create an Image Classification. 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. 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 mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. 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. 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. This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. We determine number of training steps and the learning rate schedule by the batch size for labeled images. Self-training with Noisy Student - These CVPR 2020 papers are the Open Access versions, provided by the. If nothing happens, download Xcode and try again. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. To achieve this result, we first train an EfficientNet model on labeled This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. Abdominal organ segmentation is very important for clinical applications. 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. It is expensive and must be done with great care. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. 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.