  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.
