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.

Original languageEnglish (US)
Title of host publicationWS-17-01
Subtitle of host publicationArtificial Intelligence and Operations Research for Social Good; WS-17-02: Artificial Intelligence, Ethics, and Society; WS-17-03: Artificial Intelligence for Connected and Automated Vehicles; WS-17-04: Artificial Intelligence for Cyber Security; WS-17-05: Artificial Intelligence for Smart Grids and Buildings; WS-17-06: Computer Poker and Imperfect Information Games; WS-17-07: Crowdsourcing, Deep Learning and Artificial Intelligence Agents; WS-17-08: Distributed Machine Learning; WS-17-09: Joint Workshop on Health Intelligence; WS-17-10: Human-Aware Artificial Intelligence; WS-17-11: Human-Machine Collaborative Learning; WS-17-12: Knowledge-Based Techniques for Problem Solving and Reasoning; WS-17-13: Plan, Activity, and Intent Recognition; WS-17-14: Symbolic Inference and Optimization; WS-17-15: What's Next for AI in Games?
PublisherAI Access Foundation
Pages774-780
Number of pages7
ISBN (Electronic)9781577357865
StatePublished - Jan 1 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: Feb 4 2017Feb 10 2017

Publication series

NameAAAI Workshop - Technical Report
VolumeWS-17-01 - WS-17-15

Other

Other31st AAAI Conference on Artificial Intelligence, AAAI 2017
CountryUnited States
CitySan Francisco
Period2/4/172/10/17

ASJC Scopus subject areas

  • Engineering(all)

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    Demakethepalli Venkateswara, H., Chakraborty, S., McDaniel, T., & Panchanathan, S. (2017). Model selection with nonlinear embedding for unsupervised domain adaptation. In WS-17-01: Artificial Intelligence and Operations Research for Social Good; WS-17-02: Artificial Intelligence, Ethics, and Society; WS-17-03: Artificial Intelligence for Connected and Automated Vehicles; WS-17-04: Artificial Intelligence for Cyber Security; WS-17-05: Artificial Intelligence for Smart Grids and Buildings; WS-17-06: Computer Poker and Imperfect Information Games; WS-17-07: Crowdsourcing, Deep Learning and Artificial Intelligence Agents; WS-17-08: Distributed Machine Learning; WS-17-09: Joint Workshop on Health Intelligence; WS-17-10: Human-Aware Artificial Intelligence; WS-17-11: Human-Machine Collaborative Learning; WS-17-12: Knowledge-Based Techniques for Problem Solving and Reasoning; WS-17-13: Plan, Activity, and Intent Recognition; WS-17-14: Symbolic Inference and Optimization; WS-17-15: What's Next for AI in Games? (pp. 774-780). (AAAI Workshop - Technical Report; Vol. WS-17-01 - WS-17-15). AI Access Foundation.