论文标题
结构化预测，通过临界损失进行部分标记
Structured Prediction with Partial Labelling through the Infimum Loss
论文作者
论文摘要
注释数据集是当今监督学习的主要成本之一。弱监督的目的是使模型仅使用标签的形式学习，这些标签的收集价格便宜，作为部分标签。这是一种不完整的注释，在每个数据点中，监督都被视为包含真实元素的一组标签。已经研究了针对分类，多标签，排名或细分等特定实例的部分标签的监督学习问题，但仍然缺少一般框架。本文提供了一个基于结构化预测和最大损失概念的统一框架，以处理广泛的学习问题和损失功能的部分标签。该框架自然导致了可以轻松实施的明确算法，并证明这是统计一致性和学习率。实验证实了所提出的方法比常用基线的优越性。
Annotating datasets is one of the main costs in nowadays supervised learning. The goal of weak supervision is to enable models to learn using only forms of labelling which are cheaper to collect, as partial labelling. This is a type of incomplete annotation where, for each datapoint, supervision is cast as a set of labels containing the real one. The problem of supervised learning with partial labelling has been studied for specific instances such as classification, multi-label, ranking or segmentation, but a general framework is still missing. This paper provides a unified framework based on structured prediction and on the concept of infimum loss to deal with partial labelling over a wide family of learning problems and loss functions. The framework leads naturally to explicit algorithms that can be easily implemented and for which proved statistical consistency and learning rates. Experiments confirm the superiority of the proposed approach over commonly used baselines.
