Discovering interpretable latent space directions of gans beyond binary attributes

• Main Authors: YANG, Huiting, CHAI, Liangyu, WEN, Qiang, ZHAO, Shuang, SUN, Zixun, HE, Shengfeng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Attribute data; Binary attributes; Freeforms; High-fidelity images; Image space; Image transformations; Latent vectors; Learn+; Positive data; Space directions; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8521; https://ink.library.smu.edu.sg/context/sis_research/article/9524/viewcontent/Discovering_Interpretable_Latent_Space_Directions_of_GANs_Beyond_Binary_Attributes.pdf
• Institution: Singapore Management University

Generative adversarial networks (GANs) learn to map noise latent vectors to high- fidelity image outputs. It is found that the input latent space shows semantic correlations with the output image space. Recent works aim to interpret the latent space and discover meaningful directions that correspond...