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|a Ellis, Kevin
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Ritchie, Daniel
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|a Solar-Lezama, Armando
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|a Tenenbaum, Joshua B.
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|a Learning to Infer Graphics Programs from Hand-Drawn Images
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|c 2021-11-08T21:02:24Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/137831
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|a © 2018 Curran Associates Inc.All rights reserved. We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of LAT E X. 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 a specification (spec) of what the graphics program needs to draw. We learn a model that uses program synthesis techniques to recover a graphics program from that spec. 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.
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