Fix the Noise: Disentangling Source Feature for Controllable Domain Translation
Dongyeun Lee, Jae Young Lee, Doyeon Kim, Jaehyun Choi, Jaejun Yoo, Junmo Kim
2023年份
2顶会引用
摘要
Figure 1 . Given a source model Gs, our method can smoothly control the degree of source domain features in a fine-tuned model Gs→t. Samples in each row are generated from the same latent code z ∈ Z by Gs and Gs→t. Here, Hs, Hs→t, and Ht denote feature spaces of the source, our model, and simply fine-tuned target model, respectively. Our approach explicitly guides the model to preserve the source features by using the anchor point n anch , which allows a flexible and smooth cross-domain control via Gs→t.
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引用它的顶会 Paper2
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- Few-Shot Unsupervised Image-to-Image TranslationMing-Yu Liu, Xun Huang, Arun Mallya, Tero Karras 等ICCV 2019 · 被引用 668 次
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- High-Fidelity GAN Inversion for Image Attribute EditingTengfei Wang, Yong Zhang, Yanbo Fan, Jue Wang 等CVPR 2022 · 被引用 227 次
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