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.

翻译：元学习旨在获取有效的初始化模型，已成为处理未见任务的重要方法。然而，当元学习器试图覆盖广泛的任务分布（例如跨不同数据集或领域的学习）时，其局限性显而易见。近期，一批研究工作尝试使用多个模型初始化来覆盖广泛任务，但在自适应扩展初始化方面存在局限。我们提出XB-MAML，该方法学习可扩展的基参数，通过线性组合这些基参数形成针对给定任务的有效初始化。XB-MAML通过观测基向量张成的空间与微调参数之间的差异，决定是否扩展基。我们的方法在多领域元学习基准测试中超越了现有工作，并开辟了元学习获取多样化归纳偏差的新途径——这些偏差可组合延伸至对多样未见任务的有效初始化。