@inproceedings{6291902d0cd94e30b968249951344c7e,
title = "Nonlinear embedding transform for unsupervised domain adaptation",
abstract = "The problem of domain adaptation (DA) deals with adapting classifier models trained on one data distribution to different data distributions. In this paper, we introduce the Nonlinear Embedding Transform (NET) for unsupervised DA by combining domain alignment along with similarity-based embedding. We also introduce a validation procedure to estimate the model parameters for the NET algorithm using the source data. Comprehensive evaluations on multiple vision datasets demonstrate that the NET algorithm outperforms existing competitive procedures for unsupervised DA.",
keywords = "MMD, Nonlinear embedding, Unsupervised, Validation",
author = "\{Demakethepalli Venkateswara\}, Hemanth and Shayok Chakraborty and Sethuraman Panchanathan",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; Computer Vision - ECCV 2016 Workshops, Proceedings ; Conference date: 08-10-2016 Through 16-10-2016",
year = "2016",
doi = "10.1007/978-3-319-49409-8\_36",
language = "English (US)",
isbn = "9783319494081",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "451--457",
editor = "Gang Hua and Herve Jegou",
booktitle = "Computer Vision {\textendash} ECCV 2016 Workshops, Proceedings",
}