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Active learning methods, like uncertainty sampling, combined with probabilistic prediction techniques have achieved success in various problems like image classification and text classification. For more complex multivariate prediction tasks, the relationships between labels play an important role in designing structured classifiers with better performance. However, computational time complexity limits prevalent probabilistic methods from effectively supporting active learning. Specifically, while non-probabilistic methods based on structured support vector ma-chines can be tractably applied to predicting cuts and bipartite matchings, conditional random fields are intractable for these structures. We propose an adversarial approach for active learning with structured prediction domains that is tractable for cuts and matching. We evaluate this approach algorithmically in two important structured prediction problems: multi-label classification and object tracking in videos. We demonstrate better accuracy and computational efficiency for our proposed method.
Author Information
Sima Behpour (University of Pennsylvania)
Anqi Liu (Caltech)
Brian Ziebart (University of Illinois at Chicago)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Poster: Active Learning for Probabilistic Structured Prediction of Cuts and Matchings »
Thu Jun 13th 01:30 -- 04:00 AM Room Pacific Ballroom
More from the Same Authors
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2018 Poster: Efficient and Consistent Adversarial Bipartite Matching »
Rizal Fathony · Sima Behpour · Xinhua Zhang · Brian Ziebart -
2018 Oral: Efficient and Consistent Adversarial Bipartite Matching »
Rizal Fathony · Sima Behpour · Xinhua Zhang · Brian Ziebart