"Exploring the auxiliary learning for long-tailed visual recognition" by Junjie Zhang, Lingqiao Liu et al.
Abstract Real-world visual data often exhibits a long-tailed distribution, where some “head” classes have a large number of samples, yet only a few samples are available for “tail” classes. The fundamental problem of learning with the imbalanced data is that insufficient training samples easily lead to the over-fitting of feature extractor and classifier for tail classes, which can be boiled down into a dilemma: on the one hand, we prefer to increase the exposure of tail class samples t...
Source: uow.edu.au