Institute of Computing Technology, Chinese Academy IR
| Asymmetric GAN for Unpaired Image-to-Image Translation | |
| Li, Yu1,2; Tang, Sheng1; Zhang, Rui1,2; Zhang, Yongdong1; Li, Jintao1; Yan, Shuicheng3,4 | |
| 2019-12-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| ISSN | 1057-7149 |
| 卷号 | 28期号:12页码:5881-5896 |
| 摘要 | Unpaired image-to-image translation problem aims to model the mapping from one domain to another with unpaired training data. Current works like the well-acknowledged Cycle GAN provide a general solution for any two domains through modeling injective mappings with a symmetric structure. While in situations where two domains are asymmetric in complexity, i.e., the amount of information between two domains is different, these approaches pose problems of poor generation quality, mapping ambiguity, and model sensitivity. To address these issues, we propose Asymmetric GAN (AsymGAN) to adapt the asymmetric domains by introducing an auxiliary variable (aux) to learn the extra information for transferring from the information-poor domain to the information-rich domain, which improves the performance of state-of-the-art approaches in the following ways. First, aux better balances the information between two domains which benefits the quality of generation. Second, the imbalance of information commonly leads to mapping ambiguity, where we are able to model one-to-many mappings by tuning aux, and furthermore, our aux is controllable. Third, the training of Cycle GAN can easily make the generator pair sensitive to small disturbances and variations while our model decouples the ill-conditioned relevance of generators by injecting aux during training. We verify the effectiveness of our proposed method both qualitatively and quantitatively on asymmetric situation, label-photo task, on Cityscapes and Helen datasets, and show many applications of asymmetric image translations. In conclusion, our AsymGAN provides a better solution for unpaired image-to-image translation in asymmetric domains. |
| 关键词 | Generative adversarial networks cycle GAN asymmetric GAN image-to-image translation unpaired translation |
| DOI | 10.1109/TIP.2019.2922854 |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | National Key Research and Development Program of China[2017YFC0820605] ; National Natural Science Foundation of China[61572472] ; National Natural Science Foundation of China[61871004] ; National Natural Science Foundation of China[61525206] ; National Natural Science Foundation of China[2019A010] |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000484306000010 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.204/handle/2XEOYT63/4754 |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Tang, Sheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 3.AI Inst, Qihoo 360, Beijing 100025, Peoples R China 4.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore |
| 推荐引用方式 GB/T 7714 | Li, Yu,Tang, Sheng,Zhang, Rui,et al. Asymmetric GAN for Unpaired Image-to-Image Translation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(12):5881-5896. |
| APA | Li, Yu,Tang, Sheng,Zhang, Rui,Zhang, Yongdong,Li, Jintao,&Yan, Shuicheng.(2019).Asymmetric GAN for Unpaired Image-to-Image Translation.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(12),5881-5896. |
| MLA | Li, Yu,et al."Asymmetric GAN for Unpaired Image-to-Image Translation".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.12(2019):5881-5896. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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