Semantic invariant cross-domain image generation with generative adversarial networks | |
Mao, Xiaofeng1; Wang, Shuhui2; Zheng, Liying1; Huang, Qingming2,3 | |
刊名 | NEUROCOMPUTING |
2018-06-07 | |
卷号 | 293页码:55-63 |
关键词 | Generative adversarial networks Image-to-image translation Semantic invariance |
ISSN号 | 0925-2312 |
DOI | 10.1016/j.neucom.2018.02.092 |
英文摘要 | Recently, thanks to the state-of-the-art techniques in Generative Adversarial Networks, a lot of work achieves remarkable performance on learning the mapping between an input image and an output image without any paired relation. However, traditional methods on image-to-image translation merely consider the visual appearance properties, they fail to maintain the true semantics of an image during the transfer learning procedure from source to target domain. We propose a new approach that utilizes GAN to translate unpaired images between domains and remain high level semantic abstraction aligned. Our model controls the hierarchical semantics of images by processing semantic information on label level and spatial level respectively by constructing label and attention consistent losses. The experimental results on several benchmark datasets show that generated samples are both visually similar with target images and semantically consistent with their source counterparts. Furthermore, the experiment also suggests that our method can effectively improve the classification performance in unsupervised domain adaptation problem. (c) 2018 Elsevier B.V. All rights reserved. |
资助项目 | National Natural Science Foundation of China[61771155] ; National Natural Science Foundation of China[61672497] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61650202] ; National Natural Science Foundation of China[U1636214] ; National Basic Research Program of China (973 Program)[2015CB351802] ; Key Research Program of Frontier Sciences of CAS[QYZDJ-SSW-SYS013] |
WOS研究方向 | Computer Science |
语种 | 英语 |
出版者 | ELSEVIER SCIENCE BV |
WOS记录号 | WOS:000429323200006 |
内容类型 | 期刊论文 |
源URL | [http://119.78.100.204/handle/2XEOYT63/5744] |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Huang, Qingming |
作者单位 | 1.Harbin Engn Univ, Coll Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intell Info Proc, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Mao, Xiaofeng,Wang, Shuhui,Zheng, Liying,et al. Semantic invariant cross-domain image generation with generative adversarial networks[J]. NEUROCOMPUTING,2018,293:55-63. |
APA | Mao, Xiaofeng,Wang, Shuhui,Zheng, Liying,&Huang, Qingming.(2018).Semantic invariant cross-domain image generation with generative adversarial networks.NEUROCOMPUTING,293,55-63. |
MLA | Mao, Xiaofeng,et al."Semantic invariant cross-domain image generation with generative adversarial networks".NEUROCOMPUTING 293(2018):55-63. |
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