Controllable image generation based on causal representation learning

Artificial intelligence generated content (AIGC) has emerged as an indispensable tool for producing large-scale content in various forms, such as images, thanks to the significant role that AI plays in imitation and production. However, interpretability and controllability remain challenges. Existin...

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Bibliographic Details
Published in:	Frontiers of information technology & electronic engineering Vol. 25; no. 1; pp. 135 - 148
Main Authors:	Huang, Shanshan, Wang, Yuanhao, Gong, Zhili, Liao, Jun, Wang, Shu, Liu, Li
Format:	Journal Article
Language:	English
Published:	Hangzhou Zhejiang University Press 2024 Springer Nature B.V
Subjects:	Artificial intelligence Communications Engineering Computer Hardware Computer Science Computer Systems Organization and Communication Networks Controllability Deep learning Electrical Engineering Electronics and Microelectronics Generative adversarial networks Graphs Image processing Instrumentation Machine learning Methods Modules Networks Optimization Random variables Representations Semantics Causal representation learning 可控图像编辑 Causal structure learning 因果结构学习 Image generation TP391.41 图像生成 Controllable image editing 因果表征学习
Online Access:	Get full text
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