TY - GEN
T1 - Model selection with nonlinear embedding for unsupervised domain adaptation
AU - Demakethepalli Venkateswara, Hemanth
AU - Chakraborty, Shayok
AU - McDaniel, Troy
AU - Panchanathan, Sethuraman
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Domain adaptation deals with adapting classifiers trained on data from a source distribution, to work effectively on data from a target distribution. In this paper, we introduce the Nonlinear Embedding Transform (NET) for unsupervised domain adaptation. The NET reduces cross-domain disparity through nonlinear domain alignment. It also embeds the domain- aligned data such that similar data points are clustered together. This results in enhanced classification. To determine the parameters in the NET model (and in other unsupervised domain adaptation models), we introduce a validation procedure by sampling source data points that are similar in distribution to the target data. We test the NET and the validation procedure using popular image datasets and compare the classification results across competitive procedures for unsupervised domain adaptation.
AB - Domain adaptation deals with adapting classifiers trained on data from a source distribution, to work effectively on data from a target distribution. In this paper, we introduce the Nonlinear Embedding Transform (NET) for unsupervised domain adaptation. The NET reduces cross-domain disparity through nonlinear domain alignment. It also embeds the domain- aligned data such that similar data points are clustered together. This results in enhanced classification. To determine the parameters in the NET model (and in other unsupervised domain adaptation models), we introduce a validation procedure by sampling source data points that are similar in distribution to the target data. We test the NET and the validation procedure using popular image datasets and compare the classification results across competitive procedures for unsupervised domain adaptation.
UR - http://www.scopus.com/inward/record.url?scp=85046095379&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85046095379
T3 - AAAI Workshop - Technical Report
SP - 774
EP - 780
BT - WS-17-01
PB - AI Access Foundation
T2 - 31st AAAI Conference on Artificial Intelligence, AAAI 2017
Y2 - 4 February 2017 through 10 February 2017
ER -