14 citations · 14 across the 4 of their papers we have counts for
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cs.CV2022
StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation
Aibek Alanov, Vadim Titov, Maksim Nakhodnov +1
Domain adaptation of GANs is a problem of fine-tuning GAN models pretrained on a large dataset (e.g. StyleGAN) to a specific domain with few samples (e.g. painting faces, sketches,…
cs.CV2022★ 14 cited
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
Aibek Alanov, Vadim Titov, Dmitry Vetrov
Domain adaptation framework of GANs has achieved great progress in recent years as a main successful approach of training contemporary GANs in the case of very limited training dat…