Work

Nonlinear Embedding Transform for Unsupervised Domain Adaptation

Year: 2017

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1706.07524

Authors: Venkateswara, Hemanth, Chakraborty, Shayok, Panchanathan, Sethuraman

Institution: Arizona State University

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2950565387

Cites: 11

Cited by:

Related to: 20