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Generative Graph Transformer

License: MIT PyTorch implementation of <i>Image-Conditioned Graph Generation for Road Network Extraction</i> (https://arxiv.org/abs/1910.14388)

Overview

This library contains a PyTorch implementation of the Generative Graph Transformer (GGT): an autoregressive, attention-based model for image-to-graph generation as presented in [1](https://arxiv.org/abs/1910.14388), in addition to other baselines discussed in the paper. Find out more about this project in our blog post.

Dependencies

See requirements.txt

Structure

Usage

Find out more about this project in our blog post. Please cite [1] in your work when using this library in your experiments.

Feedback

For questions and comments, feel free to contact Davide Belli.

Citation

[1] Belli, Davide and Kipf, Thomas (2019). Image-Conditioned Graph Generation for Road Network Extraction. NeurIPS 2019 workshop on Graph Representation Learning.

BibTeX format:

@article{belli2019image,
  title={Image-Conditioned Graph Generation for Road Network Extraction},
  author={Belli, Davide and Kipf, Thomas},
  journal={NeurIPS 2019 workshop on Graph Representation Learning},
  year={2019}
}

Copyright

Copyright © 2019 Davide Belli.

This project is distributed under the <a href="LICENSE">MIT license</a>. This was developed as part of a master thesis supervised by Thomas Kipf at the University of Amsterdam, and presented as a paper at the Graph Representation Learning workshop in NeurIPS 2019, Vancouver, Canada.