图像翻译
翻译(生物学)
计算机科学
一致性(知识库)
图像(数学)
领域(数学分析)
人工智能
特征(语言学)
生成语法
机器翻译
相互信息
自然语言处理
模式识别(心理学)
理论计算机科学
机器学习
数学
语言学
化学
哲学
数学分析
生物化学
信使核糖核酸
基因
作者
Yuxi Wang,Zhaoxiang Zhang,Wangli Hao,Chunfeng Song
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2020-11-17
卷期号:30: 670-684
被引量:12
标识
DOI:10.1109/tip.2020.3037528
摘要
The image-to-image translation aims to learn the corresponding information between the source and target domains. Several state-of-the-art works have made significant progress based on generative adversarial networks (GANs). However, most existing one-to-one translation methods ignore the correlations among different domain pairs. We argue that there is common information among different domain pairs and it is vital to multiple domain pairs translation. In this paper, we propose a unified circular framework for multiple domain pairs translation, leveraging a shared knowledge module across numerous domains. One selected translation pair can benefit from the complementary information from other pairs, and the sharing knowledge is conducive to mutual learning between domains. Moreover, absolute consistency loss is proposed and applied in the corresponding feature maps to ensure intra-domain consistency. Furthermore, our model can be trained in an end-to-end manner. Extensive experiments demonstrate the effectiveness of our approach on several complex translation scenarios, such as Thermal IR switching, weather changing, and semantic transfer tasks.
科研通智能强力驱动
Strongly Powered by AbleSci AI