RSG: A Simple but Effective Module for Learning Imbalanced Datasets
Imbalanced datasets widely exist in practice and are a great challenge for training deep neural models with a good generalization on infrequent classes. In this work, we propose a new rare-class sample generator (RSG) to solve this problem. RSG aims to generate some new samples for rare classes duri...
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| Veröffentlicht in: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 3783 - 3792 |
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01.06.2021
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| Abstract | Imbalanced datasets widely exist in practice and are a great challenge for training deep neural models with a good generalization on infrequent classes. In this work, we propose a new rare-class sample generator (RSG) to solve this problem. RSG aims to generate some new samples for rare classes during training, and it has in particular the following advantages: (1) it is convenient to use and highly versatile, because it can be easily integrated into any kind of convolutional neural network, and it works well when combined with different loss functions, and (2) it is only used during the training phase, and therefore, no additional burden is imposed on deep neural networks during the testing phase. In extensive experimental evaluations, we verify the effectiveness of RSG. Furthermore, by leveraging RSG, we obtain competitive results on Imbalanced CIFAR and new state-of-the-art results on Places-LT, ImageNet-LT, and iNaturalist 2018. The source code is available at https://github.com/Jianf-Wang/RSG. |
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| AbstractList | Imbalanced datasets widely exist in practice and are a great challenge for training deep neural models with a good generalization on infrequent classes. In this work, we propose a new rare-class sample generator (RSG) to solve this problem. RSG aims to generate some new samples for rare classes during training, and it has in particular the following advantages: (1) it is convenient to use and highly versatile, because it can be easily integrated into any kind of convolutional neural network, and it works well when combined with different loss functions, and (2) it is only used during the training phase, and therefore, no additional burden is imposed on deep neural networks during the testing phase. In extensive experimental evaluations, we verify the effectiveness of RSG. Furthermore, by leveraging RSG, we obtain competitive results on Imbalanced CIFAR and new state-of-the-art results on Places-LT, ImageNet-LT, and iNaturalist 2018. The source code is available at https://github.com/Jianf-Wang/RSG. |
| Author | Xu, Zhenghua Lukasiewicz, Thomas Cai, Jianfei Hu, Xiaolin Wang, Jianfeng |
| Author_xml | – sequence: 1 givenname: Jianfeng surname: Wang fullname: Wang, Jianfeng email: jianfeng.wang@cs.ox.ac.uk organization: University of Oxford – sequence: 2 givenname: Thomas surname: Lukasiewicz fullname: Lukasiewicz, Thomas email: thomas.lukasiewicz@cs.ox.ac.uk organization: University of Oxford – sequence: 3 givenname: Xiaolin surname: Hu fullname: Hu, Xiaolin email: xlhu@tsinghua.edu.cn organization: Tsinghua University – sequence: 4 givenname: Jianfei surname: Cai fullname: Cai, Jianfei email: jianfei.cai@monash.edu organization: Monash University – sequence: 5 givenname: Zhenghua surname: Xu fullname: Xu, Zhenghua email: zhenghua.xu@hebut.edu.cn organization: Hebei University of Technology |
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| Title | RSG: A Simple but Effective Module for Learning Imbalanced Datasets |
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