FSPool: Learning Set Representations with Featurewise Sort PoolingDownload PDF

Sep 25, 2019 (edited May 01, 2020)ICLR 2020 Conference Blind SubmissionReaders: Everyone
  • Original Pdf: pdf
  • 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.
  • Code: https://github.com/Cyanogenoid/fspool
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