ML Anthology

Diverse Image Generation via Self-Conditioned GANs

Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, Antonio Torralba
CVPR 2020
doi:10.1109/CVPR42600.2020.01429 /cvpr/2020/liu2020cvpr-diverse/

Abstract

We introduce a simple but effective unsupervised method for generating diverse images. We train a class-conditional GAN model without using manually annotated class labels. Instead, our model is conditional on labels automatically derived from clustering in the discriminator's feature space. Our clustering step automatically discovers diverse modes, and explicitly requires the generator to cover them. Experiments on standard mode collapse benchmarks show that our method outperforms several competing methods when addressing mode collapse. Our method also performs well on large-scale datasets such as ImageNet and Places365, improving both diversity and standard metrics (e.g., Frechet Inception Distance), compared to previous methods.

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Cite

Text

Liu et al. "Diverse Image Generation via Self-Conditioned GANs." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.01429

Markdown

[Liu et al. "Diverse Image Generation via Self-Conditioned GANs." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/liu2020cvpr-diverse/) doi:10.1109/CVPR42600.2020.01429

BibTeX

@inproceedings{liu2020cvpr-diverse,
  title     = {{Diverse Image Generation via Self-Conditioned GANs}},
  author    = {Liu, Steven and Wang, Tongzhou and Bau, David and Zhu, Jun-Yan and Torralba, Antonio},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2020},
  doi       = {10.1109/CVPR42600.2020.01429},
  url       = {https://mlanthology.org/cvpr/2020/liu2020cvpr-diverse/}
}