- 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/iclr-2020-anon1234/submission/