Resembled generative adversarial networks: Two domains with similar attributes

Duhyeon Bang, Hyunjung Shim

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)


We propose a novel algorithm, namely Resembled Generative Adversarial Networks (GAN), that generates two different domain data simultaneously where they resemble each other. Although recent GAN algorithms achieve the great success in learning the cross-domain relationship [9, 19, 22], their application is limited to domain transfers, which requires the input image. The first attempt to tackle the data generation of two domains was proposed by CoGAN [10]. However, their solution is inherently vulnerable for various levels of domain similarities. Unlike CoGAN, our Resembled GAN implicitly induces two generators to match feature covariance from both domains, thus leading to share semantic attributes. Hence, we effectively handle a wide range of structural and semantic similarities between various two domains. Based on experimental analysis on various datasets, we verify that the proposed algorithm is effective for generating two domains with similar attributes.

Original languageEnglish
Publication statusPublished - 2019
Event29th British Machine Vision Conference, BMVC 2018 - Newcastle, United Kingdom
Duration: 2018 Sep 32018 Sep 6


Conference29th British Machine Vision Conference, BMVC 2018
Country/TerritoryUnited Kingdom

Bibliographical note

Funding Information:
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ICT Consilience Creative Program (IITP-2018-2017-0-01015) supervised by the IITP(Institute for Information & communications Technology Promotion), the Ministry of Science and ICT, Korea (2018-0-00207, Immersive Media Research Laboratory), the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the MSIP (NRF-2016R1A2B4016236), and ICT R&D program of MSIP/IITP. [R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding]

Publisher Copyright:
© 2018. The copyright of this document resides with its authors.

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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