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Apr, 2021
提高任意风格迁移的风格感知归一化损失
Style-Aware Normalized Loss for Improving Arbitrary Style Transfer
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Jiaxin Cheng, Ayush Jaiswal, Yue Wu, Pradeep Natarajan, Prem Natarajan
TL;DR
本文研究神经风格转换中不平衡风格转移的问题,提出了一种解决方案,通过提出新的Loss function在理论分析和实验结果中证明其有效性,提高风格欺骗率和人类评估的偏好度。
Abstract
neural style transfer
(NST) has quickly evolved from single-style to infinite-style models, also known as
arbitrary style transfer
(AST). Although appealing results have been widely reported in literature, our em
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