In many applications, finding adequate labeled data to train predictive
models is a major challenge. In this work, we propose methods to use
group-level binary labels as weak supervision to train instance-level binary
classification models. Aggregate labels are common in several domains where
annotating on a group-level might be cheaper or might be the only way to
provide annotated data without infringing on privacy. We model group-level
labels as Class Conditional Noisy (CCN) labels for individual instances and use
the noisy labels to regularize predictions of the model trained on the
strongly-labeled instances. Our experiments on real-world application of land
cover mapping shows the utility of the proposed method in leveraging
group-level labels, both in the presence and absence of class imbalance.

本文提出使用群体级别二元标签作为弱监督来训练示例级别二元分类模型的方法，并将群体级别标签建模为个体实例的条件嘈杂标签，并使用嘈杂标签来调整在强标记实例上训练的模型的预测。实验结果表明，该方法对地表覆盖映射等真实应用具有实用价值，无论类别不平衡是否存在。