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Art Style Transfer using different Neural Networks | by Javier Nogueira | Jul, 2020

1: Matthew D. Zeiler, Rob Fergus, “Visualizing and Understanding Convolutional Networks” (2013),

2: Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, “A Neural Algorithm of Artistic Style” (2015),

3: Karen Simonyan, Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition” (2014),

4: Justin Johnson, Alexandre Alahi, Li Fei-Fei, “Perceptual Losses for Real-Time Style Transfer and Super-Resolution” (2016),

5: Alec Radford, Luke Metz, Soumith Chintala, “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks” (2015),

6: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, “Deep Residual Learning for Image Recognition” (2015),

7: Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens, “Exploring the structure of a real-time, arbitrary neural artistic stylization network” (2017),

8: Xun Huang, Serge Belongie, “Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization” (2017),

9: Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna, “Rethinking the Inception Architecture for Computer Vision” (2015),

10: Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur, “A Learned Representation For Artistic Style” (2016),

11: Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang, “Universal Style Transfer via Feature Transforms” (2017),

12: Chris Olah, Alexander Mordvintsev, Ludwig Schubert, “Feature Visualization” (2017),