TY - GEN
T1 - Bidirectional adversarial domain adaptation with semantic consistency
AU - Zhang, Yaping
AU - Nie, Shuai
AU - Liang, Shan
AU - Liu, Wenju
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Unsupervised domain adaptation DA aims to utilize the well-annotated source domain data to recognize the unlabeled target domain data that usually have a large domain shift. Most existing DA methods are developed to align the high-level feature-space distribution between the source and target domains, while neglecting the semantic consistency and low-level pixel-space information. In this paper, we propose a novel bidirectional adversarial domain adaptation BADA method to simultaneously adapt the pixel-level and feature-level shifts with semantic consistency. To keep semantic consistency, we propose a soft label-based semantic consistency constraint, which takes advantage of the well-trained source classifier during bidirectional adversarial mappings. Furthermore, the semantic consistency has been first analyzed during the domain adaptation with regard to both qualitative and quantitative evaluation. Systematic experiments on four benchmark datasets show that the proposed BADA achieves the state-of-the-art performance.
AB - Unsupervised domain adaptation DA aims to utilize the well-annotated source domain data to recognize the unlabeled target domain data that usually have a large domain shift. Most existing DA methods are developed to align the high-level feature-space distribution between the source and target domains, while neglecting the semantic consistency and low-level pixel-space information. In this paper, we propose a novel bidirectional adversarial domain adaptation BADA method to simultaneously adapt the pixel-level and feature-level shifts with semantic consistency. To keep semantic consistency, we propose a soft label-based semantic consistency constraint, which takes advantage of the well-trained source classifier during bidirectional adversarial mappings. Furthermore, the semantic consistency has been first analyzed during the domain adaptation with regard to both qualitative and quantitative evaluation. Systematic experiments on four benchmark datasets show that the proposed BADA achieves the state-of-the-art performance.
KW - Domain adaptation
KW - GAN
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85084392430&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-31726-3_16
DO - 10.1007/978-3-030-31726-3_16
M3 - Conference Proceeding
AN - SCOPUS:85084392430
SN - 9783030317256
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 184
EP - 198
BT - Pattern Recognition and Computer Vision- 2nd Chinese Conference, PRCV 2019, Proceedings, Part III
A2 - Lin, Zhouchen
A2 - Wang, Liang
A2 - Tan, Tieniu
A2 - Yang, Jian
A2 - Shi, Guangming
A2 - Zheng, Nanning
A2 - Chen, Xilin
A2 - Zhang, Yanning
PB - Springer
T2 - 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019
Y2 - 8 November 2019 through 11 November 2019
ER -