ICLR 2020poster94 citations
FSPool: Learning Set Representations with Featurewise Sort Pooling
Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
Abstract
Traditional set prediction models can struggle with simple datasets due to an issue we call the responsibility problem. We introduce a pooling method for sets of feature vectors based on sorting features across elements of the set. This can be used to construct a permutation-equivariant auto-encoder that avoids this responsibility problem. On a toy dataset of polygons and a set version of MNIST, we show that such an auto-encoder produces considerably better reconstructions and representations. Replacing the pooling function in existing set encoders with FSPool improves accuracy and convergence speed on a variety of datasets.
set auto-encoderset encoderpooling
BibTeX
@inproceedings{
Zhang2020FSPool:,
title={FSPool: Learning Set Representations with Featurewise Sort Pooling},
author={Yan Zhang and Jonathon Hare and Adam Prügel-Bennett},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=HJgBA2VYwH}
}