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

Hassan Gharoun; Fereshteh Momenifar; Fang Chen; Amir H. Gandomi

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arXiv · 2303.07502

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

Hassan Gharoun·Fereshteh Momenifar·Fang Chen·Amir H. Gandomi

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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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Hassan Gharoun, Fereshteh Momenifar, Fang Chen, Amir H. Gandomi. 2023-03-13. Meta-learning approaches for few-shot learning: A survey of recent advances. https://arxiv.org/abs/2303.07502

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