#7354. A metric-based meta-learning approach combined attention mechanism and ensemble learning for few-shot learning

October 2026publication date
Proposal available till 28-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Electrical and Electronic Engineering;
Hardware and Architecture;
Human-Computer Interaction;
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Abstract:
Meta-learning is one of the latest research directions in machine learning, which is considered to be one of the most probably ways to realize strong artificial intelligence. Meta-learning focuses on seeking solutions for machines to learn like human beings do - to recognize things through only few sample data and quickly adapt to new tasks. Challenges occur in how to train an efficient machine model with limited labeled data, since the model is easily over-fitted. In this paper, we address this obvious but important problem and propose a metric-based meta-learning model, which combines attention mechanisms and ensemble learning method.
Keywords:
Attention module; Ensemble learning; Few-shot learning; Meta-learning; Metric-learning

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