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Jun, 2023
ICDaeLST: 轻量级快速风格转移的强度可调细节增强
ICDaeLST: Intensity-Controllable Detail Attention-enhanced for Lightweight Fast Style Transfer
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Jiang Shi Qi
TL;DR
我们引入了一种轻量级快速的风格迁移模型ICDaeLST,该模型采用简单、浅层和小型结构,结合风格鉴别器、全局语义不变性损失和浅层细节注意力增强模块,实现更好的风格迁移效果和更快的处理速度。
Abstract
The mainstream
style transfer
methods usually use pre-trained deep convolutional
neural network
(VGG) models as encoders, or use more complex model structures to achieve better
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