FSPool: Learning Set Representations with Featurewise Sort PoolingDownload PDF

25 Sept 2019, 19:15 (modified: 01 May 2020, 09:38)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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Code: https://github.com/Cyanogenoid/fspool
Keywords: set auto-encoder, set encoder, pooling
TL;DR: Sort in encoder and undo sorting in decoder to avoid responsibility problem in set auto-encoders
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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