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 . 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.
|Publication status||Published - 2019|
|Event||29th British Machine Vision Conference, BMVC 2018 - Newcastle, United Kingdom|
Duration: 2018 Sep 3 → 2018 Sep 6
|Conference||29th British Machine Vision Conference, BMVC 2018|
|Period||18/9/3 → 18/9/6|
Bibliographical noteFunding 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]
© 2018. The copyright of this document resides with its authors.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition