Faster Optimization-Based Meta-Learning Adaptation Phase

TL;DR

This paper introduces Lambda patterns to optimize the adaptation phase of MAML, significantly reducing adaptation time and improving accuracy with minimal quality loss by selectively skipping gradient computations.

Contribution

It proposes Lambda pattern-based selection to accelerate MAML adaptation, a novel approach to improve efficiency and accuracy in meta-learning.

Findings

Adaptation time decreased by a factor of 3

Minimal accuracy loss with faster adaptation

Improved one-step adaptation accuracy

Abstract

Neural networks require a large amount of annotated data to learn. Meta-learning algorithms propose a way to decrease the number of training samples to only a few. One of the most prominent optimization-based meta-learning algorithms is Model-Agnostic Meta-Learning (MAML). However, the key procedure of adaptation to new tasks in MAML is quite slow. In this work we propose an improvement to MAML meta-learning algorithm. We introduce Lambda patterns by which we restrict which weight are updated in the network during the adaptation phase. This makes it possible to skip certain gradient computations. The fastest pattern is selected given an allowed quality degradation threshold parameter. In certain cases, quality improvement is possible by a careful pattern selection. The experiments conducted have shown that via Lambda adaptation pattern selection, it is possible to significantly improve the MAML method in the following areas: adaptation time has been decreased by a factor of 3 with minimal accuracy loss; accuracy for one-step adaptation has been substantially improved.