Class-Imbalanced Domain Adaptation - An Empirical Odyssey.

ECCV Workshops(2020)

引用 70|浏览85
暂无评分
摘要
Unsupervised domain adaptation is a promising way to generalize deep models to novel domains. However the current literature assumes that the label distribution is domain-invariant and only aligns the feature distributions or vice versa. In this work, we explore the more realistic task of Class-imbalanced Domain Adaptation: How to align feature distributions across domains while the label distributions of the two domains are also different? Taking a practical step towards this problem, we constructed its first benchmark with 22 cross-domain tasks from 6 real-image datasets. We conducted comprehensive experiments on 10 recent domain adaptation methods and find most of them are very fragile in the face of coexisting feature and label distribution shift. Towards a better solution, we further proposed a feature and label distribution CO-ALignment (COAL) model with a novel combination of existing ideas. COAL is empirically shown to outperform most recent domain adaptation methods on our benchmarks. We believe the provided benchmarks, empirical analysis results, and the COAL baseline could stimulate and facilitate future research towards this important problem.
更多
查看译文
关键词
adaptation,domain,empirical odyssey,class-imbalanced
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要