Joint alignment and compactness learning for multi-source unsupervised domain adaptation

Yuhong Zhang; Mingxuan Du; Fuzhen Zhuang; Yuxi Jin; Yuhui Ma; Hongbo Zhang

doi:10.1117/12.2623645

4 March 2022 Joint alignment and compactness learning for multi-source unsupervised domain adaptation

Yuhong Zhang, Mingxuan Du, Fuzhen Zhuang, Yuxi Jin, Yuhui Ma, Hongbo Zhang

Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 120841E (2022) https://doi.org/10.1117/12.2623645
Event: Fourteenth International Conference on Machine Vision (ICMV 2021), 2021, Rome, Italy

Abstract

Multi-source unsupervised domain adaptation (MUDA) has received increasing attention that leverages the knowledge from multiple relevant source domains with different distributions to improve the learning performance of the target domain. The most common approach for MUDA is to perform pairwise distribution alignment between the target and each source domain. However,existing methods usually treat each source domain identically in source-source and source-target alignment, which ignores the difference of multiple source domains and may lead to imperfect alignment. In addition, these methods often neglect the samples near the classification boundaries during adaptation process, resulting in misalignment of these samples. In this paper, we propose a new framework for MUDA, named Joint Alignment and Compactness Learning (JACL). We design an adaptive weighting network to automatically adjust the importance of marginal and conditional distribution alignment, and such weights are adopted to adaptively align each pair of source-target domains. We further propose to learn intra-class compact features for some target samples that lie in boundaries to reduce the domain shift. Extensive experiments demonstrate that our method can achieve remarkable results in three datasets (Digit-five, Office-31, and Office-Home) compared to recently strong baselines.

Citation Download Citation

Yuhong Zhang, Mingxuan Du, Fuzhen Zhuang, Yuxi Jin, Yuhui Ma, and Hongbo Zhang "Joint alignment and compactness learning for multi-source unsupervised domain adaptation", Proc. SPIE 12084, Fourteenth International Conference on Machine Vision (ICMV 2021), 120841E (4 March 2022); https://doi.org/10.1117/12.2623645

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KEYWORDS

Statistical modeling

Object recognition

Detection and tracking algorithms

Neural networks

Visualization

Gallium nitride

Image visualization

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