@techreport{TD:101552,
	att_abstract={{In pattern recognition and computer vision, one is often faced
with scenarios where the training data used to learn a model
have different distribution from the data on which the model
is applied. Regardless of the cause, any distributional change
that occurs after learning a classifier can degrade its performance
at test time. Domain adaptation tries to mitigate this degradation.
In this article, we provide a survey of domain adaptation
methods for visual recognition. We discuss the merits and drawbacks
of existing domain adaptation approaches and identify
promising avenues for research in this rapidly evolving field}},
	att_authors={rg4675},
	att_categories={},
	att_copyright={{IEEE}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2015. {{, 2015-05-01}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101552},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101552_DS1_2015-01-06T19:43:07.049Z.pdf},
	author={Raghuraman Gopalan and Vishal Patel and Ruonan Li and Rama Chellappa},
	institution={{NOW Publishers, IEEE Signal Processing Magazine}},
	month={May},
	title={{Visual Domain Adaptation: An Overview of Recent Advances}},
	year=2015,
}