ML Anthology

StarGAN V2: Diverse Image Synthesis for Multiple Domains

Yunjey Choi, Youngjung Uh, Jaejun Yoo, Jung-Woo Ha
CVPR 2020
doi:10.1109/CVPR42600.2020.00821 /cvpr/2020/choi2020cvpr-stargan/

Abstract

A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains. We propose StarGAN v2, a single framework that tackles both and shows significantly improved results over the baselines. Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate our superiority in terms of visual quality, diversity, and scalability. To better assess image-to-image translation models, we release AFHQ, high-quality animal faces with large inter- and intra-domain differences. The code, pretrained models, and dataset are available at https://github.com/clovaai/stargan-v2.

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Cite

Text

Choi et al. "StarGAN V2: Diverse Image Synthesis for Multiple Domains." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00821

Markdown

[Choi et al. "StarGAN V2: Diverse Image Synthesis for Multiple Domains." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/choi2020cvpr-stargan/) doi:10.1109/CVPR42600.2020.00821

BibTeX

@inproceedings{choi2020cvpr-stargan,
  title     = {{StarGAN V2: Diverse Image Synthesis for Multiple Domains}},
  author    = {Choi, Yunjey and Uh, Youngjung and Yoo, Jaejun and Ha, Jung-Woo},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2020},
  doi       = {10.1109/CVPR42600.2020.00821},
  url       = {https://mlanthology.org/cvpr/2020/choi2020cvpr-stargan/}
}