AttGAN: Facial Attribute Editing by Only Changing What You Want

doi:10.1109/TIP.2019.2916751

	AttGAN: Facial Attribute Editing by Only Changing What You Want
	He, Zhenliang 1,2; Zuo, Wangmeng 3; Kan, Meina 1,2; Shan, Shiguang 1,2,4,5; Chen, Xilin 1,2
	2019-11-01
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	28 期号:11 页码:5464-5478
摘要	Facial attribute editing aims to manipulate single or multiple attributes on a given face image, i.e., to generate a new face image with desired attributes while preserving other details. Recently, the generative adversarial net (GAN) and encoder-decoder architecture are usually incorporated to handle this task with promising results. Based on the encoder-decoder architecture, facial attribute editing is achieved by decoding the latent representation of a given face conditioned on the desired attributes. Some existing methods attempt to establish an attribute-independent latent representation for further attribute editing. However, such attribute-independent constraint on the latent representation is excessive because it restricts the capacity of the latent representation and may result in information loss, leading to over-smooth or distorted generation. Instead of imposing constraints on the latent representation, in this work, we propose to apply an attribute classification constraint to the generated image to just guarantee the correct change of desired attributes, i.e., to change what you want. Meanwhile, the reconstruction learning is introduced to preserve attribute-excluding details, in other words, to only change what you want. Besides, the adversarial learning is employed for visually realistic editing. These three components cooperate with each other forming an effective framework for high quality facial attribute editing, referred as AttGAN. Furthermore, the proposed method is extended for attribute style manipulation in an unsupervised manner. Experiments on two wild datasets, CelebA and LFW, show that the proposed method outperforms the state-of-the-art on realistic attribute editing with other facial details well preserved.
关键词	Facial attribute editing attribute style manipulation adversarial learning
DOI	10.1109/TIP.2019.2916751
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2017YFA0700800] ; Natural Science Foundation of China[61671182] ; Natural Science Foundation of China[61772496] ; Natural Science Foundation of China[61732004]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000482600600017
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：452[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4732
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shan, Shiguang
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, Beijing 100049, Peoples R China 3.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China 4.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China 5.Peng Cheng Lab, Shenzhen 518055, Peoples R China
推荐引用方式 GB/T 7714	He, Zhenliang,Zuo, Wangmeng,Kan, Meina,et al. AttGAN: Facial Attribute Editing by Only Changing What You Want[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(11):5464-5478.
APA	He, Zhenliang,Zuo, Wangmeng,Kan, Meina,Shan, Shiguang,&Chen, Xilin.(2019).AttGAN: Facial Attribute Editing by Only Changing What You Want.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(11),5464-5478.
MLA	He, Zhenliang,et al."AttGAN: Facial Attribute Editing by Only Changing What You Want".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.11(2019):5464-5478.