Sparsely-labeled source assisted domain adaptation

作者：

Highlights：

• Firstly, we consider a new yet practical DA scenario, called sparsely-labeled source assisted domain adaptation.

• Secondly, we propose a unified framework to jointly seek cluster centroids, source and target labels, and domain-invariant features.

• Thirdly, we construct an optimization strategy to solve the objective function efficiently.

摘要

•Firstly, we consider a new yet practical DA scenario, called sparsely-labeled source assisted domain adaptation.•Secondly, we propose a unified framework to jointly seek cluster centroids, source and target labels, and domain-invariant features.•Thirdly, we construct an optimization strategy to solve the objective function efficiently.

论文关键词：Domain adaptation,Sparsely-labeled source,Semi-supervised clustering,Label propagation

论文评审过程：Received 22 December 2019, Revised 16 November 2020, Accepted 22 December 2020, Available online 8 January 2021, Version of Record 15 January 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107803