Domain consistency regularization for unsupervised multi-source domain adaptive classification

作者：

Highlights：

• We propose a novel multi-source domain adaptation method for classification.

• We leverage domain consistency regularization for effective feature alignment.

• An adaptive weighting strategy is designed to tackle negative transfer.

• The proposed method achieves state-of-the-art performance on multiple benchmarks.

摘要

•We propose a novel multi-source domain adaptation method for classification.•We leverage domain consistency regularization for effective feature alignment.•An adaptive weighting strategy is designed to tackle negative transfer.•The proposed method achieves state-of-the-art performance on multiple benchmarks.

论文关键词：Domain adaptation,Transfer learning,Adversarial learning,Feature alignment

论文评审过程：Received 8 April 2021, Revised 4 May 2022, Accepted 2 August 2022, Available online 3 August 2022, Version of Record 8 August 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108955