ADIFT: Zero-Shot Generative Model Adaption Via Adaptive Domain-Invariant Feature Transfer-Reference-Cited by-同舟云学术

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ADIFT: Zero-Shot Generative Model Adaption Via Adaptive Domain-Invariant Feature Transfer

Published:2024-04-14 Issue: Volume:29 Page:2890-2894
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Wang Chaofei¹,Zhao Xiangan¹,Wang Kai²,Wu Shuai¹,Xiao Jiayu³,Geng Guotong¹

Affiliation:

1. Academy of Military Sciences,Center for Information Research,Beijing,China

2. Academy of Military Sciences,National Innovation Institute of Defense Technology,Beijing,China

3. Chinese Academy of Sciences,Institute of Computing Technology,Beijing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10445877.pdf?arnumber=10445877

Reference15 articles.

1. Learning transferable visual models from natural language supervision;Radford

2. StyleGAN-NADA

3. Matching Local Self-Similarities across Images and Videos

4. Style Transfer by Relaxed Optimal Transport and Self-Similarity

5. Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment

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