  This work introduces a novel system for the generation of images that contain
multiple classes of objects. Recent work in Generative Adversarial Networks
have produced high quality images, but many focus on generating images of a
single object or set of objects. Our system addresses the task of image
generation conditioned on a list of desired classes to be included in a single
image. This enables our system to generate images with any given combination of
objects, all composed into a visually realistic natural image. The system
learns the interrelationships of all classes represented in a dataset, and can
generate diverse samples including a set of these classes. It displays the
ability to arrange these objects together, accounting for occlusions and
inter-object spatial relations that characterize complex natural images. To
accomplish this, we introduce a novel architecture based on Conditional Deep
Convolutional GANs that is stabilized against collapse relative to both mode
and condition. The system learns to rectify mode collapse during training,
self-correcting to avoid suboptimal generation modes.
