Uniform Sampling over Episode Difficulty

Sebastien Arnold, Guneet Singh Dhillon, Avinash Ravichandran, Stefano Soatto

Keywords: episode sampling, few-shot learning, meta-learning

TL;DR: How should we sample episodes to improve generalization? Our empirical study suggests that sampling uniformly over episode difficulty outperforms curriculum learning, easy, and hard mining.

Abstract: Episodic training is a core ingredient of few-shot learning to train models on tasks with limited labelled data. Despite its success, episodic training remains largely understudied, prompting us to ask the question: what is the best way to sample episodes? In this paper, we first propose a method to approximate episode sampling distributions based on their difficulty. Building on this method, we perform an extensive analysis and find that sampling uniformly over episode difficulty outperforms other sampling schemes, including curriculum and easy-/hard-mining. As the proposed sampling method is algorithm agnostic, we can leverage these insights to improve few-shot learning accuracies across many episodic training algorithms. We demonstrate the efficacy of our method across popular few-shot learning datasets, algorithms, network architectures, and protocols.

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Code: http://seba1511.net/projects/eis/

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/uniform-sampling-over-episode-difficulty/code)