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arXiv · 2310.14413

Data Augmentation: a Combined Inductive-Deductive Approach featuring Answer Set Programming

Pierangela Bruno·Francesco Calimeri·Cinzia Marte·Simona Perri
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Abstract

Although the availability of a large amount of data is usually given for granted, there are relevant scenarios where this is not the case; for instance, in the biomedical/healthcare domain, some applications require to build huge datasets of proper images, but the acquisition of such images is often hard for different reasons (e.g., accessibility, costs, pathology-related variability), thus causing limited and usually imbalanced datasets. Hence, the need for synthesizing photo-realistic images via advanced Data Augmentation techniques is crucial. In this paper we propose a hybrid inductive-deductive approach to the problem; in particular, starting from a limited set of real labeled images, the proposed framework makes use of logic programs for declaratively specifying the structure of new images, that is guaranteed to comply with both a set of constraints coming from the domain knowledge and some specific desiderata. The resulting labeled images undergo a dedicated process based on Deep Learning in charge of creating photo-realistic images that comply with the generated label.

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Pierangela Bruno, Francesco Calimeri, Cinzia Marte, Simona Perri. 2023-10-22. Data Augmentation: a Combined Inductive-Deductive Approach featuring Answer Set Programming. https://arxiv.org/abs/2310.14413

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