This is the official implementation of our paper Deep Set Prediction Networks. 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.
To use the decoder, you only need dspn.py
.
You can see how it is used in model.py
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 page.
@article{zhang2019dspn,
author = {Yan Zhang and Jonathon Hare and Adam Pr\"ugel-Bennett},
title = {Deep Set Prediction Networks},
year = {2019},
eprint = {1906.06565},
url = {https://arxiv.org/abs/1906.06565}
}