DAGAN: A Domain-Aware Method for Image-to-Image Translations

Xu Yin, Yan Li and Byeong-Seok Shin

Complexity, 2020, vol. 2020, 1-15

Abstract:

The image-to-image translation method aims to learn inter-domain mappings from paired/unpaired data. Although this technique has been widely used for visual predication tasks—such as classification and image segmentation—and achieved great results, we still failed to perform flexible translations when attempting to learn different mappings, especially for images containing multiple instances. To tackle this problem, we propose a generative framework DAGAN (Domain-aware Generative Adversarial etwork) that enables domains to learn diverse mapping relationships. We assumed that an image is composed with background and instance domain and then fed them into different translation networks. Lastly, we integrated the translated domains into a complete image with smoothed labels to maintain realism. We examined the instance-aware framework on datasets generated by YOLO and confirmed that this is capable of generating images of equal or better diversity compared to current translation models.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/8503/2020/9341907.pdf (application/pdf)
http://downloads.hindawi.com/journals/8503/2020/9341907.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:9341907

DOI: 10.1155/2020/9341907

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().