AgileGAN: Stylizing Portraits by Inversion-Consistent Transfer Learning

By Li Luo. 2021

Subfields: Generative Models

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Generative Models

1. Daniel Cohen-Or
2. Rinon Gal
3. Yuval Alaluf
4. Or Patashnik
5. Amit H. Bermano
6. Eli Shechtman
7. Dani Lischinski
8. Zongze Wu

Citations in the corpus

• StyleGAN-NADA (2021, methodology): “First, due to our intertwined generator architecture and the nature of the directional loss – and second, because prior methods successfully employed the natural alignment of a finetuned generator for downstream applications [31, 37, 44].”
• Third Time's the Charm? Image and Video Editing with StyleGAN3 (2022, background): “Multiple works leverage the fine-tuning of StyleGAN2 generators for various applications, such as image-to-image translation [21,52,64,74,84] and inversion [7,10,56].”