Bidirectional adversarial domain adaptation with semantic consistency

Yaping Zhang; Shuai Nie; Shan Liang; Wenju Liu

doi:10.1007/978-3-030-31726-3_16

Bidirectional adversarial domain adaptation with semantic consistency

Yaping Zhang, Shuai Nie, Shan Liang, Wenju Liu^*

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

3 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Pattern Recognition and Computer Vision- 2nd Chinese Conference, PRCV 2019, Proceedings, Part III
Editors	Zhouchen Lin, Liang Wang, Tieniu Tan, Jian Yang, Guangming Shi, Nanning Zheng, Xilin Chen, Yanning Zhang
Publisher	Springer
Pages	184-198
Number of pages	15
ISBN (Print)	9783030317256
DOIs	https://doi.org/10.1007/978-3-030-31726-3_16
Publication status	Published - 2019
Externally published	Yes
Event	2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019 - Xi’an, China Duration: 8 Nov 2019 → 11 Nov 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11859 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019
Country/Territory	China
City	Xi’an
Period	8/11/19 → 11/11/19

Keywords

Domain adaptation
GAN
Unsupervised learning

Access to Document

10.1007/978-3-030-31726-3_16

Cite this

Zhang, Y., Nie, S., Liang, S., & Liu, W. (2019). Bidirectional adversarial domain adaptation with semantic consistency. In Z. Lin, L. Wang, T. Tan, J. Yang, G. Shi, N. Zheng, X. Chen, & Y. Zhang (Eds.), Pattern Recognition and Computer Vision- 2nd Chinese Conference, PRCV 2019, Proceedings, Part III (pp. 184-198). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11859 LNCS). Springer. https://doi.org/10.1007/978-3-030-31726-3_16

Zhang, Yaping ; Nie, Shuai ; Liang, Shan et al. / Bidirectional adversarial domain adaptation with semantic consistency. Pattern Recognition and Computer Vision- 2nd Chinese Conference, PRCV 2019, Proceedings, Part III. editor / Zhouchen Lin ; Liang Wang ; Tieniu Tan ; Jian Yang ; Guangming Shi ; Nanning Zheng ; Xilin Chen ; Yanning Zhang. Springer, 2019. pp. 184-198 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ddb341de75154618989350c3be3790bf,

title = "Bidirectional adversarial domain adaptation with semantic consistency",

abstract = "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.",

keywords = "Domain adaptation, GAN, Unsupervised learning",

author = "Yaping Zhang and Shuai Nie and Shan Liang and Wenju Liu",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019 ; Conference date: 08-11-2019 Through 11-11-2019",

year = "2019",

doi = "10.1007/978-3-030-31726-3\_16",

language = "English",

isbn = "9783030317256",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "184--198",

editor = "Zhouchen Lin and Liang Wang and Tieniu Tan and Jian Yang and Guangming Shi and Nanning Zheng and Xilin Chen and Yanning Zhang",

booktitle = "Pattern Recognition and Computer Vision- 2nd Chinese Conference, PRCV 2019, Proceedings, Part III",

}

Zhang, Y, Nie, S, Liang, S & Liu, W 2019, Bidirectional adversarial domain adaptation with semantic consistency. in Z Lin, L Wang, T Tan, J Yang, G Shi, N Zheng, X Chen & Y Zhang (eds), Pattern Recognition and Computer Vision- 2nd Chinese Conference, PRCV 2019, Proceedings, Part III. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11859 LNCS, Springer, pp. 184-198, 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019, Xi’an, China, 8/11/19. https://doi.org/10.1007/978-3-030-31726-3_16

Bidirectional adversarial domain adaptation with semantic consistency. / Zhang, Yaping; Nie, Shuai; Liang, Shan et al.
Pattern Recognition and Computer Vision- 2nd Chinese Conference, PRCV 2019, Proceedings, Part III. ed. / Zhouchen Lin; Liang Wang; Tieniu Tan; Jian Yang; Guangming Shi; Nanning Zheng; Xilin Chen; Yanning Zhang. Springer, 2019. p. 184-198 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11859 LNCS).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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 - https://www.scopus.com/pages/publications/85084392430

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 -

Zhang Y, Nie S, Liang S, Liu W. Bidirectional adversarial domain adaptation with semantic consistency. In Lin Z, Wang L, Tan T, Yang J, Shi G, Zheng N, Chen X, Zhang Y, editors, Pattern Recognition and Computer Vision- 2nd Chinese Conference, PRCV 2019, Proceedings, Part III. Springer. 2019. p. 184-198. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-31726-3_16

Bidirectional adversarial domain adaptation with semantic consistency

Abstract

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Keywords

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