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arXiv · 1411.1752

Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets

Adarsh Prasad·Stefanie Jegelka·Dhruv Batra
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Abstract

To cope with the high level of ambiguity faced in domains such as Computer Vision or Natural Language processing, robust prediction methods often search for a diverse set of high-quality candidate solutions or proposals. In structured prediction problems, this becomes a daunting task, as the solution space (image labelings, sentence parses, etc.) is exponentially large. We study greedy algorithms for finding a diverse subset of solutions in structured-output spaces by drawing new connections between submodular functions over combinatorial item sets and High-Order Potentials (HOPs) studied for graphical models. Specifically, we show via examples that when marginal gains of submodular diversity functions allow structured representations, this enables efficient (sub-linear time) approximate maximization by reducing the greedy augmentation step to inference in a factor graph with appropriately constructed HOPs. We discuss benefits, tradeoffs, and show that our constructions lead to significantly better proposals.

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BibTeXRIS

Adarsh Prasad, Stefanie Jegelka, Dhruv Batra. 2014-11-06. Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets. https://arxiv.org/abs/1411.1752

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