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Long-tailed Recognition by Routing Diverse Distribution-Aware Experts | | Xudong Wang
; Long Lian
; Zhongqi Miao
; Ziwei Liu
; Stella X. Yu
; | | Date: |
5 Oct 2020 | | Abstract: | Natural data are often long-tail distributed over semantic classes. Existing
recognition methods tend to focus on tail performance gain, often at the
expense of head performance loss from increased classifier variance. The low
tail performance manifests itself in large inter-class confusion and high
classifier variance. We aim to reduce both the bias and the variance of a
long-tailed classifier by RoutIng Diverse Experts (RIDE). It has three
components: 1) a shared architecture for multiple classifiers (experts); 2) a
distribution-aware diversity loss that encourages more diverse decisions for
classes with fewer training instances; and 3) an expert routing module that
dynamically assigns more ambiguous instances to additional experts. With on-par
computational complexity, RIDE significantly outperforms the state-of-the-art
methods by 5% to 7% on all the benchmarks including CIFAR100-LT, ImageNet-LT
and iNaturalist. RIDE is also a universal framework that can be applied to
different backbone networks and integrated into various long-tailed algorithms
and training mechanisms for consistent performance gains. | | Source: | arXiv, 2010.01809 | | Services: | Forum | Review | PDF | Favorites | |
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