A one-to-many conditional generative adversarial network framework for multiple image-to-image translations
作者: Chunlei ChaiJing LiaoNing ZouLingyun Sun
作者单位: 1Laboratory of CAD&CG, Zhejiang University
刊名: Multimedia Tools and Applications, 2018, Vol.77 (17), pp.22339-22366
来源数据库: Springer Nature Journal
DOI: 10.1007/s11042-018-5968-7
关键词: Image-to-image translationGenerative adversarial networkOne-to-many conditional generative adversarial networkDeep learning
原始语种摘要: Abstract(#br)Image-to-Image translation was proposed as a general form of many image learning problems. While generative adversarial networks were successfully applied on many image-to-image translations, many models were limited to specific translation tasks and were difficult to satisfy practical needs. In this work, we introduce a One-to-Many conditional generative adversarial network, which could learn from heterogeneous sources of images. This is achieved by training multiple generators against a discriminator in synthesized learning way. This framework supports generative models to generate images in each source, so output images follow corresponding target patterns. Two implementations, hybrid fake and cascading learning, of the synthesized adversarial training scheme are also...
全文获取路径: Springer Nature  (合作)
影响因子:1.014 (2012)