Learning Feature Alignment Architecture for Domain Adaptation

Zhixiong Yue1; Pengxin Guo2; Yu Zhang3; Christy Liang1

10.1109/IJCNN55064.2022.9892615

2022

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

2161-4393

978-1-6654-9526-4

2022 International Joint Conference on Neural Networks (IJCNN)

1-8

18-23 July 2022

Padua, Italy

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

In domain adaptation, where the feature distributions of the source and target domains are different, various distance-based methods have been proposed to handle the domain shift by minimizing the discrepancy between the source and target domains. These methods use hand-crafted bottleneck networks, which might hinder the alignment of hidden feature representations extracted from both domains. In this paper, we propose a new method called Alignment Architecture Search with Population Correlation (AASPC) to automatically learn the architecture of the bottleneck network that can align the source and target domains. The proposed AASPC method introduces a new similarity function called Population Correlation (PC) to measure the domain discrepancy. The proposed AASPC method leverages PC to learn the alignment architecture and domaininvariant feature representation. Experiments on several benchmark datasets, including Office-31, Office-Home, and VisDA2017, show the effectiveness of the proposed AASPC method.

domain adaptation neural architecture search transfer learning

其他

英语

CPCI-S

Shenzhen fundamental research program[JCYJ20210324105000003] ; NSFC[62076118]

Computer Science ; Engineering ; Neurosciences & Neurology

Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic ; Neurosciences

WOS:000867070905125

IEEE

1.School of Computer Science, University of Technology Sydney
2.Department of Computer Science and Engineering, Southern University of Science and Technology
3.Peng Cheng Laboratory

Zhixiong Yue,Pengxin Guo,Yu Zhang,et al. Learning Feature Alignment Architecture for Domain Adaptation[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:1-8.