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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 to learn better set representations. This can be used to construct a permutation-equivariant auto-encoder, which avoids the 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. Used in set classification, FSPool significantly improves accuracy and convergence speed on the set versions of MNIST and CLEVR.
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 (2019) FSPool: Learning set representations with featurewise sort pooling. Sets & Partitions: NeurIPS 2019 Workshop, Vancouver Convention Centre, West Level 2, #215-216, Canada. 14 Dec 2019.

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 to learn better set representations. This can be used to construct a permutation-equivariant auto-encoder, which avoids the 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. Used in set classification, FSPool significantly improves accuracy and convergence speed on the set versions of MNIST and CLEVR.

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Accepted/In Press date: 30 September 2019
Published date: 14 December 2019
Venue - Dates: Sets & Partitions: NeurIPS 2019 Workshop, Vancouver Convention Centre, West Level 2, #215-216, Canada, 2019-12-14 - 2019-12-14

Identifiers

Local EPrints ID: 434824
URI: http://eprints.soton.ac.uk/id/eprint/434824
PURE UUID: 79d35f1a-dfc8-4ffe-b25a-1b5aaa0c311f
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: 11 Oct 2019 16:30
Last modified: 14 Aug 2020 01:45

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Contributors

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

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