  Generative adversarial networks (GANs) have been trained to be professional
artists able to create stunning artworks such as face generation and image
style transfer. In this paper, we focus on a realistic business scenario:
automated generation of customizable icons given desired mobile applications
and theme styles. We first introduce a theme-application icon dataset, termed
AppIcon, where each icon has two orthogonal theme and app labels. By
investigating a strong baseline StyleGAN2, we observe mode collapse caused by
the entanglement of the orthogonal labels. To solve this challenge, we propose
IconGAN composed of a conditional generator and dual discriminators with
orthogonal augmentations, and a contrastive feature disentanglement strategy is
further designed to regularize the feature space of the two discriminators.
Compared with other approaches, IconGAN indicates a superior advantage on the
AppIcon benchmark. Further analysis also justifies the effectiveness of
disentangling app and theme representations. Our project will be released at:
https://github.com/architect-road/IconGAN.
