Batch Decorrelation for Active Metric Learning

• URL: http://arxiv.org/abs/2005.10008v2
• Date: Sat, 23 May 2020 12:52:04 GMT
• Title: Batch Decorrelation for Active Metric Learning
• Authors: Priyadarshini K, Ritesh Goru, Siddhartha Chaudhuri and Subhasis Chaudhuri
• Abstract summary: We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments. In contrast to prior work on class-based learning, we focus on em metrics that express the em degree of (dis)similarity between objects.
• Score: 21.99577268213412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments: object $x_i$ is more similar to object $x_j$ than to $x_k$. In contrast to prior work on class-based learning, where the fundamental goal is classification and any implicit or explicit metric is binary, we focus on {\em perceptual} metrics that express the {\em degree} of (dis)similarity between objects. We find that standard active learning approaches degrade when annotations are requested for {\em batches} of triplets at a time: our studies suggest that correlation among triplets is responsible. In this work, we propose a novel method to {\em decorrelate} batches of triplets, that jointly balances informativeness and diversity while decoupling the choice of heuristic for each criterion. Experiments indicate our method is general, adaptable, and outperforms the state-of-the-art.

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