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

• URL: http://arxiv.org/abs/2403.06768v1
• Date: Mon, 11 Mar 2024 14:37:57 GMT
• Title: XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage
• Authors: Jae-Jun Lee, Sung Whan Yoon
• Abstract summary: 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.
• Score: 12.38102349597265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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