TY - GEN
T1 - Certain and consistent domain adaptation
AU - Nagabandi, Bhadrinath
AU - Dudley, Andrew
AU - Venkateswara, Hemanth
AU - Panchanathan, Sethuraman
N1 - Funding Information:
Acknowledgements. The authors thank ASU, and the National Science Foundation
Funding Information:
The authors thank ASU, and the National Science Foundation for their funding support. This material is partially based upon work supported by the National Science Foundation under Grant No. 1828010.
Funding Information:
for their funding support. This material is partially based upon work supported by the National Science Foundation under Grant No. 1828010.
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Unsupervised domain adaptation algorithms seek to transfer knowledge from labeled source datasets in order to predict the labels for target datasets in the presence of domain-shift. In this paper we propose the Certain and Consistent Domain Adaptation (CCDA) model for unsupervised domain adaptation. The CCDA aligns the source and target domains using adversarial training and reduces the domain adaptation problem to a semi supervised learning (SSL) problem. We estimate the target labels using consistency regularization and entropy minimization on the domain-aligned target samples whose predictions are consistent across multiple stochastic perturbations. We evaluate the CCDA on benchmark datasets and demonstrate that it outperforms competitive baselines from domain adaptation literature.
AB - Unsupervised domain adaptation algorithms seek to transfer knowledge from labeled source datasets in order to predict the labels for target datasets in the presence of domain-shift. In this paper we propose the Certain and Consistent Domain Adaptation (CCDA) model for unsupervised domain adaptation. The CCDA aligns the source and target domains using adversarial training and reduces the domain adaptation problem to a semi supervised learning (SSL) problem. We estimate the target labels using consistency regularization and entropy minimization on the domain-aligned target samples whose predictions are consistent across multiple stochastic perturbations. We evaluate the CCDA on benchmark datasets and demonstrate that it outperforms competitive baselines from domain adaptation literature.
KW - Consistency regularization
KW - Domain adaptation
KW - Entropy regularization
KW - Semi supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85089618521&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-54407-2_29
DO - 10.1007/978-3-030-54407-2_29
M3 - Conference contribution
AN - SCOPUS:85089618521
SN - 9783030544065
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 341
EP - 356
BT - Smart Multimedia - 2nd International Conference, ICSM 2019, Revised Selected Papers
A2 - McDaniel, Troy
A2 - Berretti, Stefano
A2 - Curcio, Igor D.D.
A2 - Basu, Anup
PB - Springer
T2 - 2nd International Conference on Smart Multimedia, ICSM 2019
Y2 - 16 December 2019 through 18 December 2019
ER -