mlsanityData to 14 Oct 2026

StarGAN v2: Diverse Image Synthesis for Multiple Domains

arXiv 1912.01865 ↗First posted 4 Dec 2019cs.CV cs.LGcited by 1,098

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Cited by 250 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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 can be found at https://github.com/clovaai/stargan-v2.

Key facts. Cited by 1,098 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Dec 2019.

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Citing papers per year: 2021: 185; 2022: 221; 2023: 258; 2024: 192; 2025: 142; 2026: 100.

1,098 papers.

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