The Majority Can Help the Minority: Context-rich Minority Oversampling for Long-tailed Classification
The Majority Can Help the Minority: Context-rich Minority Oversampling for Long-tailed Classification
The problem of class imbalanced data is that the gener-alization performance of the classifier deteriorates due to the lack of data from minority classes. In this paper, we pro-pose a novel minority over-sampling method to augment di-versified minority samples by leveraging the rich context of the majority classes as background …