Neural Information Processing Systems http://nips.cc/

Wepresentgroupequivariant capsule networks,aframeworktointroduce guaranteed equivariance and invariance properties to the capsule network idea.

In this paper we propose a generalization of this work that generally exhibits improved performace, but from an implementation point of view is actually simpler.

However, their performance in terms of test likelihood and quality of generated samples has been surpassed by autoregressive models without stochastic units. Furthermore, flow-based models have recently been shown to be an attractive alternative that scales well to high-dimensional data.

These mechanisms typically retrieve items in a single step and are fixed after training.

Neural Information Processing Systems http://nips.cc/

We show that adversarial training is unnecessary and sometimes counter-productive; we instead cast invariant representation learning asasingle information-theoretic objectivethat can bedirectly optimized.

Frequently,transformations occurring in data can be better represented by a subset of a group than by agroup asawhole, e.g., rotations in[ 90,90 ]. Insuch cases, amodel that respects equivariancepartially is better suited to represent the data.