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Recently, attentional arbitrary style transfer methods have been proposed to achieve fine-grained results, which manipulates the point-wise similarity between content and style features for stylization.
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Karen Simonyan and Andrew Zisserman · 2014
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Texture synthesis using convolutional neural networks
Leon Gatys, Alexander S Ecker, and Matthias Bethge · 2015
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Fast patch-based style transfer of arbitrary style
Tian Qi Chen and Mark Schmidt · 2016
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Precomputed real-time texture synthesis with markovian generative adversarial networks
Chuan Li and Michael Wand · 2016
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Kiri Nichol · 2016
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Texture networks: Feed-forward synthesis of textures and stylized images
Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, and Victor S Lempitsky · 2016
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Stylebank: An explicit representation for neural image style transfer
Dongdong Chen, Lu Yuan, Jing Liao, Nenghai Yu, and Gang Hua · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
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Diversified texture synthesis with feed-forward networks
Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, and Ming-Hsuan Yang · 2017
Style transfer by relaxed optimal transport and self-similarity
Nicholas Kolkin, Jason Salavon, and Gregory Shakhnarovich · 2019
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Content and style disentanglement for artistic style transfer
Dmytro Kotovenko, Artsiom Sanakoyeu, Sabine Lang, and Bjorn Ommer · 2019
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Learning linear transformations for fast image and video style transfer
Xueting Li, Sifei Liu, Jan Kautz, and Ming-Hsuan Yang · 2019
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Arbitrary style transfer with style-attentional networks
Dae Young Park and Kwang Hee Lee · 2019
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Attention-aware multi-stroke style transfer
Yuan Yao, Jianqiang Ren, Xuansong Xie, Weidong Liu, Yong-Jin Liu, and Jun Wang · 2019
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Arbitrary video style transfer via multi-channel correlation
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Universal style transfer via feature transforms
Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, and Ming-Hsuan Yang · 2017
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Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2017
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Multimodal transfer: A hierarchical deep convolutional neural network for fast artistic style transfer
Xin Wang, Geoffrey Oxholm, Da Zhang, and Yuan-Fang Wang · 2017
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Arbitrary style transfer with deep feature reshuffle
Shuyang Gu, Congliang Chen, Jing Liao, and Lu Yuan · 2018
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Avatar-net: Multi-scale zero-shot style transfer by feature decoration
Lu Sheng, Ziyi Lin, Jing Shao, and Xiaogang Wang · 2018
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Multi-style generative network for real-time transfer
Hang Zhang and Kristin Dana · 2018
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Yingying Deng, Fan Tang, Weiming Dong, Haibin Huang, Chongyang Ma, and Changsheng Xu · 2020
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Arbitrary style transfer via multi-adaptation network
Yingying Deng, Fan Tang, Weiming Dong, Wen Sun, Feiyue Huang, and Changsheng Xu · 2020
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Dynamic instance normalization for arbitrary style transfer
Yongcheng Jing, Xiao Liu, Yukang Ding, Xinchao Wang, Errui Ding, Mingli Song, and Shilei Wen · 2020
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Histogan: Controlling colors of gan-generated and real images via color histograms
Mahmoud Afifi, Marcus A Brubaker, and Michael S Brown · 2021
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Manifold alignment for semantically aligned style transfer
Jing Huo, Shiyin Jin, Wenbin Li, Jing Wu, Yu-Kun Lai, Yinghuan Shi, and Yang Gao · 2021
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Adaattn: Revisit attention mechanism in arbitrary neural style transfer
Songhua Liu, Tianwei Lin, Dongliang He, Fu Li, Meiling Wang, Xin Li, Zhengxing Sun, Qian Li, and Errui Ding · 2021
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Fast optimal transport artistic style transfer
Ting Qiu, Bingbing Ni, Ziang Liu, and Xuanhong Chen · 2021
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