TY - GEN
T1 - Multiple Subspace Alignment Improves Domain Adaptation
AU - Thopalli, Kowshik
AU - Anirudh, Rushil
AU - Thiagarajan, Jayaraman J.
AU - Turaga, Pavan
N1 - Funding Information:
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-PROC-767906. KT and PT were supported in part by ARO grant number W911NF-17-1-0293 and NSF CAREER award 1451263.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - We present a novel unsupervised domain adaptation (DA) method for cross-domain visual recognition. Though subspace methods have found success in DA, their performance is often limited due to the assumption of approximating an entire dataset using a single low-dimensional subspace. Instead, we develop a method to effectively represent the source and target datasets via a collection of low-dimensional subspaces, and subsequently align them by exploiting the natural geometry of the space of subspaces, on the Grassmann manifold. We demonstrate the effectiveness of this approach, using empirical studies on two widely used benchmarks,with performance on par or better than the performance of the state of the art domain adaptation methods.
AB - We present a novel unsupervised domain adaptation (DA) method for cross-domain visual recognition. Though subspace methods have found success in DA, their performance is often limited due to the assumption of approximating an entire dataset using a single low-dimensional subspace. Instead, we develop a method to effectively represent the source and target datasets via a collection of low-dimensional subspaces, and subsequently align them by exploiting the natural geometry of the space of subspaces, on the Grassmann manifold. We demonstrate the effectiveness of this approach, using empirical studies on two widely used benchmarks,with performance on par or better than the performance of the state of the art domain adaptation methods.
KW - Domain Adaptation
KW - Grassmann manifold
KW - Unsupervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85068958606&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068958606&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8682420
DO - 10.1109/ICASSP.2019.8682420
M3 - Conference contribution
AN - SCOPUS:85068958606
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3552
EP - 3556
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -