  Current approaches for predicting sets from feature vectors ignore the
unordered nature of sets and suffer from discontinuity issues as a result. We
propose a general model for predicting sets that properly respects the
structure of sets and avoids this problem. With a single feature vector as
input, we show that our model is able to auto-encode point sets, predict the
set of bounding boxes of objects in an image, and predict the set of attributes
of these objects.
