Name
Abstract | ||
|---|---|---|
ABSTRACTGenerative 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3503161.3548109 | International Multimedia Conference |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yinpeng Chen | 1 | 0 | 0.34 |
| Zhiyu Pan | 2 | 0 | 0.34 |
| Min Shi | 3 | 0 | 0.34 |
| Hao Lu | 4 | 0 | 1.01 |
| Zhiguo Cao | 5 | 314 | 44.17 |
| Weicai Zhong | 6 | 0 | 1.01 |