Batch Decorrelation for Active Metric Learning [article]

Priyadarshini K, Ritesh Goru, Siddhartha Chaudhuri, Subhasis Chaudhuri
2020 arXiv   pre-print
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 perceptual metrics that express the degree of (dis)similarity between objects. We find that standard active learning approaches degrade when annotations
more » ... requested for batches of triplets at a time: our studies suggest that correlation among triplets is responsible. In this work, we propose a novel method to 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.
arXiv:2005.10008v2 fatcat:5dacudmo25didelhmaav2peo3m