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.
@article{arxiv.2303.07502,
title = {Meta-learning approaches for few-shot learning: A survey of recent advances},
author = {Hassan Gharoun and Fereshteh Momenifar and Fang Chen and Amir H. Gandomi},
journal= {arXiv preprint arXiv:2303.07502},
year = {2023}
}