Discovering interpretable latent space directions of gans beyond binary attributes

Discovering interpretable latent space directions of gans beyond binary attributes

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...

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Main Authors:	YANG, Huiting, CHAI, Liangyu, WEN, Qiang, ZHAO, Shuang, SUN, Zixun, HE, Shengfeng
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2021
主題:	Attribute data Binary attributes Freeforms High-fidelity images Image space Image transformations Latent vectors Learn+ Positive data Space directions Databases and Information Systems
在線閱讀:	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
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