Meta-learning approaches for few-shot learning: A survey of recent advances
It provides a comprehensive overview for researchers working on few-shot learning, but it is incremental as it surveys existing methods without introducing new techniques.
This survey tackles the problem of deep learning's poor generalization to new tasks with few samples by reviewing meta-learning approaches, covering recent advances in metric-based, memory-based, and learning-based methods.
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.