Generalized Coverage for More Robust Low-Budget Active Learning

Wonho Bae; Junhyug Noh; Danica J. Sutherland

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arXiv · 2407.12212

Generalized Coverage for More Robust Low-Budget Active Learning

Wonho Bae·Junhyug Noh·Danica J. Sutherland

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Abstract

The ProbCover method of Yehuda et al. is a well-motivated algorithm for active learning in low-budget regimes, which attempts to "cover" the data distribution with balls of a given radius at selected data points. We demonstrate, however, that the performance of this algorithm is extremely sensitive to the choice of this radius hyper-parameter, and that tuning it is quite difficult, with the original heuristic frequently failing. We thus introduce (and theoretically motivate) a generalized notion of "coverage," including ProbCover's objective as a special case, but also allowing smoother notions that are far more robust to hyper-parameter choice. We propose an efficient greedy method to optimize this coverage, generalizing ProbCover's algorithm; due to its close connection to kernel herding, we call it "MaxHerding." The objective can also be optimized non-greedily through a variant of $k$-medoids, clarifying the relationship to other low-budget active learning methods. In comprehensive experiments, MaxHerding surpasses existing active learning methods across multiple low-budget image classification benchmarks, and does so with less computational cost than most competitive methods.

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BibTeX RIS

Wonho Bae, Junhyug Noh, Danica J. Sutherland. 2024-07-24. Generalized Coverage for More Robust Low-Budget Active Learning. https://arxiv.org/abs/2407.12212

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