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A Closer Look at the Training Strategy for Modern Meta-Learning

JIAXIN CHEN · Xiao-Ming Wu · Yanke Li · Qimai LI · Li-Ming Zhan · Fu-lai Chung

Poster Session 4 #1311

Keywords: [ Relational Learning ] [ Algorithms ] [ Representation Learning ] [ Algorithms -> Metric Learning; Algorithms ]

Abstract: The support/query (S/Q) episodic training strategy has been widely used in modern meta-learning algorithms and is believed to improve their generalization ability to test environments. This paper conducts a theoretical investigation of this training strategy on generalization. From a stability perspective, we analyze the generalization error bound of generic meta-learning algorithms trained with such strategy. We show that the S/Q episodic training strategy naturally leads to a counterintuitive generalization bound of $O(1/\sqrt{n})$, which only depends on the task number $n$ but independent of the inner-task sample size $m$. Under the common assumption $m<

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