  Images with shared characteristics naturally form sets. For example, in a
face verification benchmark, images of the same identity form sets. For
generative models, the standard way of dealing with sets is to represent each
as a one hot vector, and learn a conditional generative model
$p(\mathbf{x}|\mathbf{y})$. This representation assumes that the number of sets
is limited and known, such that the distribution over sets reduces to a simple
multinomial distribution. In contrast, we study a more generic problem where
the number of sets is large and unknown. We introduce Set Distribution Networks
(SDNs), a novel framework that learns to autoencode and freely generate sets.
We achieve this by jointly learning a set encoder, set discriminator, set
generator, and set prior. We show that SDNs are able to reconstruct image sets
that preserve salient attributes of the inputs in our benchmark datasets, and
are also able to generate novel objects/identities. We examine the sets
generated by SDN with a pre-trained 3D reconstruction network and a face
verification network, respectively, as a novel way to evaluate the quality of
generated sets of images.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning