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Open Access Publications from the University of California

Learning part-based abstractions for visual object concepts


The ability to represent semantic structure in the environment — objects, parts, and relations — is a core aspect of human visual perception and cognition. Here we leverage recent advances in program synthesis to develop an algorithm for learning the part-based structure of drawings as represented by graphics programs. This algorithm iteratively learns a library of abstract subroutines that can be used to more compactly represent a set of drawings by capturing common structural elements. Our experiments explore how this algorithm exploits statistical reg- ularities across drawings to learn new subroutines. Together, these findings highlight the potential for understanding human visual concept learning via program-like abstractions.

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