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Learning to Infer Graphics Programs from Hand-Drawn Images
Name
7845-learning-to-infer-graphics-programs-from-hand-drawn-images.pdf
Description
Published version
Size
1.46 MB
Format
Adobe PDF
Checksum (MD5)
0b91aa0b84307cf9bbb2607fe8a0b7cf
Author(s)
Ellis, Kevin
Ritchie, Daniel
Solar-Lezama, Armando
Tenenbaum, Joshua B.
Date Issued
2018
Citation
Ellis, Kevin, Ritchie, Daniel, Solar-Lezama, Armando and Tenenbaum, Joshua B. 2018. "Learning to Infer Graphics Programs from Hand-Drawn Images."
Version
Final published version
Abstract
© 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/7845-learning-to-infer-graphics-programs-from-hand-drawn-images
