Informative discriminator for domain adaptation

作者：

Highlights：

• Proposemethodtousesourceinformationinascalablewayfordomaindiscriminator

• Show that it helps to preserve the target samples mode information.

• Propose a novel Sample Section Module

• Provides additional insights into understanding our method

• Results includes hierarchical class labels and statistical significance tests

• Discrepancy distance and feature visualization for detailed analysis comprehensively

摘要

•Proposemethodtousesourceinformationinascalablewayfordomaindiscriminator•Show that it helps to preserve the target samples mode information.•Propose a novel Sample Section Module•Provides additional insights into understanding our method•Results includes hierarchical class labels and statistical significance tests•Discrepancy distance and feature visualization for detailed analysis comprehensively

论文关键词：CNN,Domain adaptation,Adversarial learning,Discriminator,Ensemble method,Object recognition

论文评审过程：Received 25 August 2020, Revised 31 March 2021, Accepted 3 April 2021, Available online 20 April 2021, Version of Record 5 May 2021.

论文官网地址：https://doi.org/10.1016/j.imavis.2021.104180