Brain-inspired semantic data augmentation for multi-style images

Data augmentation is an effective technique for automatically expanding training data in deep learning. Brain-inspired methods are approaches that draw inspiration from the functionality and structure of the human brain and apply these mechanisms and principles to artificial intelligence and compute...

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Principais autores:	Wei Wang, Zhaowei Shang, Chengxing Li
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Frontiers Media S.A. 2024-03-01
סדרה:	Frontiers in Neurorobotics
נושאים:	data augmentation deep learning robust statistics style transfer uncertainty modeling brain-inspired computer vision
גישה מקוונת:	https://www.frontiersin.org/articles/10.3389/fnbot.2024.1382406/full
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