Weakly Supervised Classification Using Group-Level Labels

• URL: http://arxiv.org/abs/2108.07330v1
• Date: Mon, 16 Aug 2021 20:01:45 GMT
• Title: Weakly Supervised Classification Using Group-Level Labels
• Authors: Guruprasad Nayak, Rahul Ghosh, Xiaowei Jia, Vipin Kumar
• Abstract summary: We propose methods to use group-level binary labels as weak supervision to train instance-level binary classification models. 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.
• Score: 12.285265254225166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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