A convex Kullback-Leibler divergence and critical-descriptor prototypes for semi-supervised few-shot learning

Yukun Liu; Daming Shi

doi:10.1007/s10489-025-06239-1

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A convex Kullback-Leibler divergence and critical-descriptor prototypes for semi-supervised few-shot learning

Yukun Liu Daming Shi

Year: 2025 Journal: Applied Intelligence Vol: 55 (5) Publisher: Springer Science+Business Media

DOI: 10.1007/s10489-025-06239-1

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Keywords:

Computer science Kullback–Leibler divergence Divergence (linguistics) Shot (pellet) Artificial intelligence Regular polygon Pattern recognition (psychology) Mathematics Geometry

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Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

Machine Learning and ELM

Physical Sciences → Computer Science → Artificial Intelligence

Geophysical Methods and Applications

Physical Sciences → Engineering → Ocean Engineering

A convex Kullback-Leibler divergence and critical-descriptor prototypes for semi-supervised few-shot learning

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