Paper Title
Advancements in Meta-Learning Paradigms: A Comprehensive Exploration of Techniques for Few-Shot Learning in Computer Vision
Abstract
The rapid evolution of computer vision applications demands innovative approaches to address the challenges
posed by limited labelled data. This study undertakes a comprehensive exploration of meta-learning paradigms, specifically
focusing on their efficacy in the realm of few-shot learning. Three distinct meta-learning frameworks (MAML, Reptile and
Matching networks) are scrutinised and compared to elucidate their contributions to the adaptability and generalisation
capabilities of computer vision models. This research contributes valuable insights to the meta-learning discourse in
computer vision, offering nuanced understandings of framework strengths and limitations and serving as a catalyst for future
innovations in adaptive machine learning models.
Keywords - Meta-Learning, Few-Shot Learning, Computer Vision, Adaptive Learning, Model Generalization, Meta-
Learning Frameworks