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Significant strides have been made toward designing better generative modelsin recent years. Despite this progress, however, state-of-the-art approachesare still largely unable to capture complex global structure in data. Forexample, images of buildings typically contain spatial patterns such as windowsrepeating at regular intervals; state-of-the-art generative methods can'teasily reproduce these structures. We propose to address this problem byincorporating programs representing global structure into the generativemodel---e.g., a 2D for-loop may represent a configuration of windows.Furthermore, we propose a framework for learning these models by leveragingprogram synthesis to generate training data. On both synthetic and real-worlddata, we demonstrate that our approach is substantially better than thestate-of-the-art at both generating and completing images that contain globalstructure.

Learning Neurosymbolic Generative Models via Program Synthesis