Meta-learning approaches for few-shot learning: A survey of recent advances

• URL: http://arxiv.org/abs/2303.07502v1
• Date: Mon, 13 Mar 2023 22:20:39 GMT
• Title: Meta-learning approaches for few-shot learning: A survey of recent advances
• Authors: Hassan Gharoun, Fereshteh Momenifar, Fang Chen, and Amir H. Gandomi
• Abstract summary: Despite its success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks. Deep learning is notorious for poor generalization from few samples. This survey first briefly introduces meta-learning and then investigates state-of-the-art meta-learning methods.
• Score: 12.052118555436081
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction. Moreover, deep learning is notorious for poor generalization from few samples. Meta-learning is a promising approach that addresses these issues by adapting to new tasks with few-shot datasets. This survey first briefly introduces meta-learning and then investigates state-of-the-art meta-learning methods and recent advances in: (I) metric-based, (II) memory-based, (III), and learning-based methods. Finally, current challenges and insights for future researches are discussed.

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