Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation
Abstract
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.
Cite
@article{arxiv.1706.07527,
title = {Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation},
author = {Hemanth Venkateswara and Shayok Chakraborty and Troy McDaniel and Sethuraman Panchanathan},
journal= {arXiv preprint arXiv:1706.07527},
year = {2017}
}
Comments
AAAI Workshops 2017