论文标题
主动度量学习的批次去相关
Batch Decorrelation for Active Metric Learning
论文作者
论文摘要
我们提出了一个主动学习策略，用于训练距离指标的参数模型，给定三重态的相似性评估：对象$ x_i $比对象$ x_j $比$ x_k $更相似。与先前的基于班级学习的工作相反，基本目标是分类，而任何隐式或明确指标都是二进制的，我们专注于表达对象之间（dis）相似性的{\ em degur}的{\ em感知}指标。我们发现，当要求对三胞胎的{\ em批次}请求注释时，标准的主动学习方法会降低：我们的研究表明，三重态之间的相关性是负责的。在这项工作中，我们提出了一种新颖的方法，以{\ em decorlate}批次批量，共同平衡信息性和多样性，同时为每个标准取消选择启发式的选择。实验表明我们的方法是一般，适应性的，并且胜过最先进的方法。
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.
