C2DAN: An Improved Deep Adaptation Network with Domain Confusion and Classifier Adaptation

Sensors

Xinyi Chen¹  Han Sun¹  Ling Wang¹  Ningzhong Liu¹  Dong Liang¹  Huiyu Zhou² 

[1] College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;School of Informatics, University of Leicester, Leicester LE1 7RH, UK;

关键词: transfer learning;    domain adaptation;    MK-MMD;    domain confusion;    classifier adaptation;    vehicle classification;   

DOI  :  10.3390/s20123606

来源: DOAJ

【 摘 要 】

Deep neural networks have been successfully applied in domain adaptation which uses the labeled data of source domain to supplement useful information for target domain. Deep Adaptation Network (DAN) is one of these efficient frameworks, it utilizes Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to align the feature distribution in a reproducing kernel Hilbert space. However, DAN does not perform very well in feature level transfer, and the assumption that source and target domain share classifiers is too strict in different adaptation scenarios. In this paper, we further improve the adaptability of DAN by incorporating Domain Confusion (DC) and Classifier Adaptation (CA). To achieve this, we propose a novel domain adaptation method named C²DAN. Our approach first enables Domain Confusion (DC) by using a domain discriminator for adversarial training. For Classifier Adaptation (CA), a residual block is added to the source domain classifier in order to learn the difference between source classifier and target classifier. Beyond validating our framework on the standard domain adaptation dataset office-31, we also introduce and evaluate on the Comprehensive Cars (CompCars) dataset, and the experiment results demonstrate the effectiveness of the proposed framework C²DAN.

【 授权许可】

Unknown