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This work introduces a novel system for the generation of images that containmultiple classes of objects. Recent work in Generative Adversarial Networkshave produced high quality images, but many focus on generating images of asingle object or set of objects. Our system addresses the task of imagegeneration conditioned on a list of desired classes to be included in a singleimage. This enables our system to generate images with any given combination ofobjects, all composed into a visually realistic natural image. The systemlearns the interrelationships of all classes represented in a dataset, and cangenerate diverse samples including a set of these classes. It displays theability to arrange these objects together, accounting for occlusions andinter-object spatial relations that characterize complex natural images. Toaccomplish this, we introduce a novel architecture based on Conditional DeepConvolutional GANs that is stabilized against collapse relative to both modeand condition. The system learns to rectify mode collapse during training,self-correcting to avoid suboptimal generation modes.

Collapse Resistant Deep Convolutional GAN for Multi-Object Image Generation