Improving greedy core-set configurations for active learning with uncertainty-scaled distances

Yuchen Li; Frank Rudzicz

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arXiv · 2202.04251

Improving greedy core-set configurations for active learning with uncertainty-scaled distances

Yuchen Li·Frank Rudzicz

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Abstract

We scale perceived distances of the core-set algorithm by a factor of uncertainty and search for low-confidence configurations, finding significant improvements in sample efficiency across CIFAR10/100 and SVHN image classification, especially in larger acquisition sizes. We show the necessity of our modifications and explain how the improvement is due to a probabilistic quadratic speed-up in the convergence of core-set loss, under assumptions about the relationship of model uncertainty and misclassification.

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Yuchen Li, Frank Rudzicz. 2022-02-09. Improving greedy core-set configurations for active learning with uncertainty-scaled distances. https://arxiv.org/abs/2202.04251

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