  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.
