  Developing algorithms that are able to generalize to a novel task given only
a few labeled examples represents a fundamental challenge in closing the gap
between machine- and human-level performance. The core of human cognition lies
in the structured, reusable concepts that help us to rapidly adapt to new tasks
and provide reasoning behind our decisions. However, existing meta-learning
methods learn complex representations across prior labeled tasks without
imposing any structure on the learned representations. Here we propose COMET, a
meta-learning method that improves generalization ability by learning to learn
along human-interpretable concept dimensions. Instead of learning a joint
unstructured metric space, COMET learns mappings of high-level concepts into
semi-structured metric spaces, and effectively combines the outputs of
independent concept learners. We evaluate our model on few-shot tasks from
diverse domains, including fine-grained image classification, document
categorization and cell type annotation on a novel dataset from a biological
domain developed in our work. COMET significantly outperforms strong
meta-learning baselines, achieving 6-15% relative improvement on the most
challenging 1-shot learning tasks, while unlike existing methods providing
interpretations behind the model's predictions.
