  The advances in deep learning have enabled machine learning methods to
outperform human beings in various areas, but it remains a great challenge for
a well-trained model to quickly adapt to a new task. One promising solution to
realize this goal is through meta-learning, also known as learning to learn,
which has achieved promising results in few-shot learning. However, current
approaches are still enormously different from human beings' learning process,
especially in the ability to extract structural and transferable knowledge.
This drawback makes current meta-learning frameworks non-interpretable and hard
to extend to more complex tasks. We tackle this problem by introducing concept
discovery to the few-shot learning problem, where we achieve more effective
adaptation by meta-learning the structure among the data features, leading to a
composite representation of the data. Our proposed method Concept-Based
Model-Agnostic Meta-Learning (COMAML) has been shown to achieve consistent
improvements in the structured data for both synthesized datasets and
real-world datasets.
