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FSPool: Learning set representations with featurewise sort pooling

FSPool: Learning set representations with featurewise sort pooling
FSPool: Learning set representations with featurewise sort pooling
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.
Zhang, Yan
0edf84ab-1e32-4239-bef6-7fe80d6bc7a7
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Zhang, Yan
0edf84ab-1e32-4239-bef6-7fe80d6bc7a7
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e

Zhang, Yan, Hare, Jonathon and Prugel-Bennett, Adam (2020) FSPool: Learning set representations with featurewise sort pooling. International Conference on Learning Representations, Millennium Hall, Ethiopia. 26 - 30 Apr 2020.

Record type: Conference or Workshop Item (Paper)

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.

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More information

Accepted/In Press date: 19 December 2019
Published date: 26 April 2020
Venue - Dates: International Conference on Learning Representations, Millennium Hall, Ethiopia, 2020-04-26 - 2020-04-30

Identifiers

Local EPrints ID: 436921
URI: http://eprints.soton.ac.uk/id/eprint/436921
PURE UUID: 83ea56fc-2baf-407a-ba9a-4749d6b36c8d
ORCID for Yan Zhang: ORCID iD orcid.org/0000-0003-3470-3663
ORCID for Jonathon Hare: ORCID iD orcid.org/0000-0003-2921-4283

Catalogue record

Date deposited: 14 Jan 2020 17:30
Last modified: 27 Jan 2020 13:51

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Contributors

Author: Yan Zhang ORCID iD
Author: Jonathon Hare ORCID iD
Author: Adam Prugel-Bennett

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