title: Learning to Infer Graphics Programs from Hand-Drawn Images
abstract:   We introduce a model that learns to convert simple hand drawings
    into graphics programs written in a subset of \LaTeX.~The model
    combines techniques from deep learning and program synthesis.  We
    learn a convolutional neural network that proposes plausible drawing
    primitives that explain an image. These drawing primitives are like
    a trace of the set of primitive commands issued by a graphics
    program. We learn a model that uses program synthesis techniques to
    recover a graphics program from that trace. These programs have
    constructs like variable bindings, iterative loops, or simple kinds
    of conditionals. With a graphics program in hand, we can correct
    errors made by the deep network and extrapolate drawings.  Taken
    together these results are a step towards agents that induce useful,
    human-readable programs from perceptual input.
authors: Kevin Ellis Daniel Ritchie Armando Solar-Lezama Joshua B. Tenenbaum
hIndex: Joshua B. Tenenbaum 108