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2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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,doi:10.1109/cvpr46437.2021.00378 fatcat:va2nrkcpyzdofod3l6wl5nmfue