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

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 wh...

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Main Authors: Xu Yin, Yan Li, Byeong-Seok Shin
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/9341907
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author Xu Yin
Yan Li
Byeong-Seok Shin
author_facet Xu Yin
Yan Li
Byeong-Seok Shin
author_sort Xu Yin
collection DOAJ
description 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.
format Article
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institution Kabale University
issn 1076-2787
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language English
publishDate 2020-01-01
publisher Wiley
record_format Article
series Complexity
spelling doaj-art-e3982e6b23d04bc99ca08bb61f4552c92025-02-03T05:59:35ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/93419079341907DAGAN: A Domain-Aware Method for Image-to-Image TranslationsXu Yin0Yan Li1Byeong-Seok Shin2Department of Computer Engineering, Inha University, Incheon 082, Republic of KoreaDepartment of Computer Engineering, Inha University, Incheon 082, Republic of KoreaDepartment of Computer Engineering, Inha University, Incheon 082, Republic of KoreaThe 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.http://dx.doi.org/10.1155/2020/9341907
spellingShingle Xu Yin
Yan Li
Byeong-Seok Shin
DAGAN: A Domain-Aware Method for Image-to-Image Translations
Complexity
title DAGAN: A Domain-Aware Method for Image-to-Image Translations
title_full DAGAN: A Domain-Aware Method for Image-to-Image Translations
title_fullStr DAGAN: A Domain-Aware Method for Image-to-Image Translations
title_full_unstemmed DAGAN: A Domain-Aware Method for Image-to-Image Translations
title_short DAGAN: A Domain-Aware Method for Image-to-Image Translations
title_sort dagan a domain aware method for image to image translations
url http://dx.doi.org/10.1155/2020/9341907
work_keys_str_mv AT xuyin daganadomainawaremethodforimagetoimagetranslations
AT yanli daganadomainawaremethodforimagetoimagetranslations
AT byeongseokshin daganadomainawaremethodforimagetoimagetranslations