Rethinking cross-domain semantic relation for few-shot image generation

Training well-performing Generative Adversarial Networks (GANs) with limited data has always been challenging. Existing methods either require sufficient data (over 100 training images) for training or generate images of low quality and low diversity. To solve this problem, we propose a new Cross-do...

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Bibliographic Details
Published in:	Applied intelligence (Dordrecht, Netherlands) Vol. 53; no. 19; pp. 22391 - 22404
Main Authors:	Gou, Yao, Li, Min, Lv, Yilong, Zhang, Yusen, Xing, Yuhang, He, Yujie
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.10.2023 Springer Nature B.V
Subjects:	Ablation Artificial Intelligence Computer Science Generative adversarial networks Image processing Image quality Machines Manufacturing Mechanical Engineering Processes Semantic relations Semantics Training Cross-domain semantic relation Generative adversarial networks Few-shot image generation Perceptual similarity Contrastive learning
ISSN:	0924-669X, 1573-7497
Online Access:	Get full text
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