Object-Oriented Learning (OOL): Perception, Representation, and Reasoning
International Conference on Machine Learning (ICML)
Friday July 17, 2020, Virtual Workshop
The ability to represent semantic structure in the environment --- objects, parts, and relations --- is a core aspect of human visual perception and cognition. As a testament to this ability, humans are capable of expressing rich conceptual knowledge in simple iconic images. Here we leverage recent advances in program synthesis to develop a self-supervised and data-efficient algorithm for learning the the part-based structure of objects, as represented by graphics programs. Our algorithm iteratively learns higher-order subroutines that can be used to more compactly represent these objects, exploiting commonalities between learned subroutines to infer a part-based grammar. Our experiments explore how the resulting library of learned subroutines is jointly determined by the data distribution and the cost of learning additional subroutines.