Compact class-conditional domain invariant learning for multi-class domain adaptation

作者：

Highlights：

• Present new generalization risk bounds for multi-class domain adaptation.

• Proposed a novel learning method based on the risk bounds.

• Performed an empirical study on benchmarking data sets.

• Considering class-conditional domain invariance delivers better performances.

摘要

•Present new generalization risk bounds for multi-class domain adaptation.•Proposed a novel learning method based on the risk bounds.•Performed an empirical study on benchmarking data sets.•Considering class-conditional domain invariance delivers better performances.

论文关键词：Domain adaptation,Generalization bound,Class-conditional domain invariant learning,PAC learning complexity,Transfer Learning

论文评审过程：Received 20 August 2019, Revised 26 September 2020, Accepted 25 November 2020, Available online 6 December 2020, Version of Record 17 December 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107763