Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning. (arXiv:2206.03996v4 [cs.LG] UPDATED)

Model-agnostic meta learning (MAML) is currently one of the dominating
approaches for few-shot meta-learning. Albeit its effectiveness, the
optimization of MAML can be challenging due to the innate bilevel problem
structure. Specifically, the loss landscape of MAML is much more complex with
possibly more saddle points and local minimizers than its empirical risk
minimization counterpart. To address this challenge, we leverage the recently
invented sharpness-aware minimization and develop a sharpness-aware MAML
approach that we term Sharp-MAML. We empirically demonstrate that …

arxiv learning lg meta model-agnostic