Data Augmentation: a Combined Inductive-Deductive Approach featuring
  Answer Set Programming
Pierangela Bruno Francesco Calimeri Cinzia Marte Simona Perri
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.