https://github.com/cyanogenoid/dspn
[NeurIPS 2019] Deep Set Prediction Networks
https://github.com/cyanogenoid/dspn
deep-learning neurips neurips-2019 prediction set
Last synced: about 1 year ago
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[NeurIPS 2019] Deep Set Prediction Networks
- Host: GitHub
- URL: https://github.com/cyanogenoid/dspn
- Owner: Cyanogenoid
- License: mit
- Created: 2019-06-14T06:06:09.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-09-18T00:50:48.000Z (over 5 years ago)
- Last Synced: 2025-04-08T15:05:48.383Z (about 1 year ago)
- Topics: deep-learning, neurips, neurips-2019, prediction, set
- Language: Python
- Homepage: https://arxiv.org/abs/1906.06565
- Size: 667 KB
- Stars: 100
- Watchers: 4
- Forks: 18
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Set Prediction Networks
This is the official implementation of our NeurIPS 2019 paper [Deep Set Prediction Networks][0].
We propose a new way of predicting sets with a neural network that doesn't suffer from discontinuity issues.
This is done by backpropagating through a set encoder to act as a set decoder.
You can take a look at the [poster for NeurIPS 2019][4] or the [poster for the NeurIPS 2019 workshop on Sets & Partitions][5].
To use the decoder, you only need [`dspn.py`][1].
You can see how it is used in [`model.py`][2] with `build_net` and the `Net` class.
For details on the exact steps to reproduce the experiments, check out the README in the `dspn` directory.
You can download pre-trained models and the predictions thereof from the [Resources][3] page.
# BibTeX entry
```
@inproceedings{zhang2019dspn,
author = {Yan Zhang and Jonathon Hare and Adam Pr\"ugel-Bennett},
title = {{Deep Set Prediction Networks}},
booktitle = {Advances in Neural Information Processing Systems 32},
year = {2019},
eprint = {1906.06565},
url = {https://arxiv.org/abs/1906.06565},
}
```
[0]: https://arxiv.org/abs/1906.06565
[1]: https://github.com/Cyanogenoid/dspn/blob/master/dspn.py
[2]: https://github.com/Cyanogenoid/dspn/blob/master/dspn/model.py
[3]: https://github.com/Cyanogenoid/dspn/releases/tag/resources
[4]: https://www.cyanogenoid.com/files/dspn-poster.pdf
[5]: https://www.cyanogenoid.com/files/dspn-workshop-poster.pdf