  Model agnostic meta-learning algorithms aim to infer priors from several
observed tasks that can then be used to adapt to a new task with few examples.
Given the inherent diversity of tasks arising in existing benchmarks, recent
methods use separate, learnable structure, such as hierarchies or graphs, for
enabling task-specific adaptation of the prior. While these approaches have
produced significantly better meta learners, our goal is to improve their
performance when the heterogeneous task distribution contains challenging
distribution shifts and semantic disparities. To this end, we introduce CAML
(Contrastive Knowledge-Augmented Meta Learning), a novel approach for
knowledge-enhanced few-shot learning that evolves a knowledge graph to
effectively encode historical experience, and employs a contrastive
distillation strategy to leverage the encoded knowledge for task-aware
modulation of the base learner. Using standard benchmarks, we evaluate the
performance of CAML in different few-shot learning scenarios. In addition to
the standard few-shot task adaptation, we also consider the more challenging
multi-domain task adaptation and few-shot dataset generalization settings in
our empirical studies. Our results shows that CAML consistently outperforms
best known approaches and achieves improved generalization.
