Generative models have become significantly more powerful in recent years. However, these models continue to have difficulty capturing global structure in data. For example, images of buildings typically contain spatial patterns such as windows repeating at regular intervals, but state-of-the-art models have difficulty generating these patterns. We propose to address this problem by incorporating programs representing global structure into generative models—e.g., a 2D for-loop may represent a repeating pattern of windows—along with a framework for learning these models by leveraging program synthesis to obtain training data. On both synthetic and real-world data, we demonstrate that our approach substantially outperforms state-of-the-art at both generating and completing images with global structure.
Halley R Young (University of Pennsylvania)
I am a second-year PhD student at University of Pennsylvania. I work in the programming languages group under the guidance of Mayur Naik. My research interests include the intersection of deep learning with programming languages, generative models of visual and musical programs, and computer science and the arts.
Osbert Bastani (University of Pennsylvania)
Mayur Naik (University of Pennsylvania)
Related Events (a corresponding poster, oral, or spotlight)
2019 Oral: Learning Neurosymbolic Generative Models via Program Synthesis »
Wed Jun 12th 05:15 -- 05:20 PM Room Hall B