Deep Learning with Sets and Point Clouds

Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos

Abstract:

We introduce a simple permutation equivariant layer for deep learning with set structure. This type of layer, obtained by parameter-sharing, has a simple implementation and linear-time complexity in the size of each set. We use deep
permutation-invariant networks to perform point-could classification and MNIST-digit summation, where in both cases the output is invariant to permutations of the input. In a semi-supervised setting, where the goal is make predictions for
each instance within a set, we demonstrate the usefulness of this type of layer in set-outlier detection as well as semi-supervised learning with clustering side-information.

Conflicts: cs.cmu.edu

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/deep-learning-with-sets-and-point-clouds/code)