  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.
