Increasing Oversampling Diversity for Long-Tailed Visual Recognition

The long-tailed data distribution in real-world greatly increases the difficulty of training deep neural networks. Oversampling minority classes is one of the commonly used techniques to tackle this problem. In this paper, we first analyze that the commonly used oversampling technique tends to distort the representation learning and harm the network’s generalizability. Then we propose two novel methods to increase the minority feature’s diversity to alleviate such issue. Specifically, from the data perspective, we propose a mixup-based Synthetic Minority Over-sampling TEchnique called mixSMOTE, where tail class samples are synthesized from head classes so that a balanced training distribution can be obtained. Then from the model perspective, we propose Gradient Re-weighting Module (GRM) to re-distribute each instance’s gradient contribution to the representation learning network. Extensive experiments on the long-tailed benchmark CIFAR10-LT, CIFAR100-LT and ImageNet-LT demonstrate the effectiveness of our proposed method.