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Semi‐supervised domain adaptation via subspace exploration

Authors :
Zheng Han
Xiaobin Zhu
Chun Yang
Zhiyu Fang
Jingyan Qin
Xucheng Yin
Source :
IET Computer Vision, Vol 18, Iss 3, Pp 370-380 (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

Abstract Recent methods of learning latent representations in Domain Adaptation (DA) often entangle the learning of features and exploration of latent space into a unified process. However, these methods can cause a false alignment problem and do not generalise well to the alignment of distributions with large discrepancy. In this study, the authors propose to explore a robust subspace for Semi‐Supervised Domain Adaptation (SSDA) explicitly. To be concrete, for disentangling the intricate relationship between feature learning and subspace exploration, the authors iterate and optimise them in two steps: in the first step, the authors aim to learn well‐clustered latent representations by aggregating the target feature around the estimated class‐wise prototypes; in the second step, the authors adaptively explore a subspace of an autoencoder for robust SSDA. Specially, a novel denoising strategy via class‐agnostic disturbance to improve the discriminative ability of subspace is adopted. Extensive experiments on publicly available datasets verify the promising and competitive performance of our approach against state‐of‐the‐art methods.

Details

Language :
English
ISSN :
17519640 and 17519632
Volume :
18
Issue :
3
Database :
Directory of Open Access Journals
Journal :
IET Computer Vision
Publication Type :
Academic Journal
Accession number :
edsdoj.907bb9d8f8754d5aa99e5b85f6c79847
Document Type :
article
Full Text :
https://doi.org/10.1049/cvi2.12254