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12 April 2021 Targeted adversarial discriminative domain adaptation
Hua-mei Chen, Andreas Savakis, Ashley Diehl, Erik Blasch, Sixiao Wei, Genshe Chen
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Proceedings Volume 11733, Geospatial Informatics XI; 117330C (2021) https://doi.org/10.1117/12.2589046
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
Abstract
Domain adaptation is a technology enabling Aided Target Recognition (AiTR) and other algorithms for environments and targets where data or labeled data is scarce. Recent advances in unsupervised domain adaptation have demonstrated excellent performance but only when the domain shift is relatively small. This paper proposes Targeted Adversarial Discriminative Domain Adaptation (T-ADDA), a semi-supervised domain adaptation method by extending the Adversarial Discriminative Domain Adaptation (ADDA) framework. By providing at least one labeled target image per class, T-ADDA significantly boosts the performance of ADDA and is applicable to the challenging scenario where the set of targets in the source and target domains are not the same. The efficacy of T-ADDA is demonstrated by several experiments using the Modified National Institute of Standards and Technology (MNIST), Street View House Numbers (SVHN), and Devanagari Handwritten Character (DHC) datasets and then extended to aerial image datasets Aerial Image Data (AID) and University of California, Merced (UCM).
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Hua-mei Chen, Andreas Savakis, Ashley Diehl, Erik Blasch, Sixiao Wei, and Genshe Chen "Targeted adversarial discriminative domain adaptation", Proc. SPIE 11733, Geospatial Informatics XI, 117330C (12 April 2021); https://doi.org/10.1117/12.2589046
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KEYWORDS
Target recognition
Airborne remote sensing
Detection and tracking algorithms
Environmental sensing
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