Skip to yearly menu bar Skip to main content


Poster

XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage

Jae-Jun Lee · SUNG WHAN YOON

MR1 & MR2 - Number 97
[ ]
Sat 4 May 6 a.m. PDT — 8:30 a.m. PDT

Abstract:

Meta-learning, which pursues an effective initialization model, has emerged as a promising approach to handling unseen tasks. However, a limitation remains to be evident when a meta-learner tries to encompass a wide range of task distribution, e.g., learning across distinctive datasets or domains. Recently, a group of works has attempted to employ multiple model initializations to cover widely-ranging tasks, but they are limited in adaptively expanding initializations. We introduce XB-MAML, which learns expandable basis parameters, where they are linearly combined to form an effective initialization to a given task. XB-MAML observes the discrepancy between the vector space spanned by the basis and fine-tuned parameters to decide whether to expand the basis. Our method surpasses the existing works in the multi-domain meta-learning benchmarks and opens up new chances of meta-learning for obtaining the diverse inductive bias that can be combined to stretch toward the effective initialization for diverse unseen tasks.

Live content is unavailable. Log in and register to view live content