A Neural Representation of Sketch Drawings
Abstract
We present sketch-rnn, a recurrent neural network able to construct stroke-based drawings of common objects. The model is trained on a dataset of human-drawn images representing many different classes. We outline a framework for conditional and unconditional sketch generation, and describe new robust training methods for generating coherent sketch drawings in a vector format.
Cite
Text
Ha and Eck. "A Neural Representation of Sketch Drawings." International Conference on Learning Representations, 2018.Markdown
[Ha and Eck. "A Neural Representation of Sketch Drawings." International Conference on Learning Representations, 2018.](https://mlanthology.org/iclr/2018/ha2018iclr-neural/)BibTeX
@inproceedings{ha2018iclr-neural,
title = {{A Neural Representation of Sketch Drawings}},
author = {Ha, David and Eck, Douglas},
booktitle = {International Conference on Learning Representations},
year = {2018},
url = {https://mlanthology.org/iclr/2018/ha2018iclr-neural/}
}