Active Learning for Structured Probabilistic Models With Histogram Approximation

Qing Sun, Ankit Laddha, Dhruv Batra; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3612-3621

Abstract


Abstract. This paper studies active learning in structured probabilistic models such as Conditional Random Fields (CRFs). This is a challenging problem because unlike unstructured prediction problems such as binary or multi-class classification, structured prediction problems involve a distribution with an exponentially-large support, for instance, over the space of all possible segmentations of an image. Thus, the entropy of such models is typically intractable to compute. We propose a crude yet surprisingly effective histogram approximation to the Gibbs distribution, which replaces the exponentially-large support with a coarsened distribution that may be viewed as a histogram over M bins. We show that our approach outperforms a number of baselines and results in a 90%-reduction in the number of annotations needed to achieve nearly the same accuracy as learning from the entire dataset.

Related Material


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[bibtex]
@InProceedings{Sun_2015_CVPR,
author = {Sun, Qing and Laddha, Ankit and Batra, Dhruv},
title = {Active Learning for Structured Probabilistic Models With Histogram Approximation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}