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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


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/√n), which only depends on the task number n but independent of the inner-task sample size m. Under the common assumption m << n for few-shot learning, the bound of O(1/√n) implies strong generalization guarantees for modern meta-learning algorithms in the few-shot regime. To further explore the influence of training strategies on generalization, we propose a leave-one-out (LOO) training strategy for meta-learning and compare it with S/Q training. Experiments on standard few-shot regression and classification tasks with popular meta-learning algorithms validate our analysis.

Original languageEnglish
Title of host publication 34th Conference on Neural Information Processing Systems, NeurIPS 2020
Publication statusPublished - Dec 2020
Event34th Conference on Neural Information Processing Systems, NeurIPS 2020 - Virtual, Online
Duration: 6 Dec 202012 Dec 2020

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference34th Conference on Neural Information Processing Systems, NeurIPS 2020
CityVirtual, Online

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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