Generalized Inner Loop Meta-Learning
This work provides a foundational framework for meta-learning researchers, though it is incremental in formalizing existing patterns.
The paper formalizes a common pattern in meta-learning called GIMLI, proves its requirements, and derives a general-purpose algorithm, along with releasing a library called higher to support future research.
Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem. In this paper, we give a formalization of this shared pattern, which we call GIMLI, prove its general requirements, and derive a general-purpose algorithm for implementing similar approaches. Based on this analysis and algorithm, we describe a library of our design, higher, which we share with the community to assist and enable future research into these kinds of meta-learning approaches. We end the paper by showcasing the practical applications of this framework and library through illustrative experiments and ablation studies which they facilitate.