Semi-Identical Twins Variational AutoEncoder for Few-Shot Learning

Data augmentation is a popular way for few-shot learning (FSL). It generates more samples as supplements and then transforms the FSL task into a common supervised learning problem for a solution. However, most data-augmentation-based FSL approaches only consider the prior visual knowledge for featur...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 35; číslo 7; s. 9455 - 9469
Hlavní autori:	Zhang, Yi, Huang, Sheng, Peng, Xi, Yang, Dan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.07.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Complementarity Conditional variational auto-encoder (CVAE) Data augmentation feature generation few-shot learning (FSL) Genetics Information processing Learning Machine learning modality absence Semantics Supervised learning Task analysis Training Twins Visualization
ISSN:	2162-237X, 2162-2388, 2162-2388
On-line prístup:	Získať plný text
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