Generative Adversarial Networks and Neural Style Transfer Approaches

Requirements

• Tensorflow

Content

• One Dimension Generative Adversarial Network (GAN)
• Deep Convolutional Generative Adversarial Networks (DCGAN)
• Conditional Generative Adversarial Network (cGAN)
• Pix2pix Image-to-Image Translation
• CycleGAN Unpaired Image-to-Image Translation
• Nueral Style Transfer (NST)
• Reconstructing Images from VGG Features
• Adaptive Instance Normalization (AdaIN)
• Deep Dream

References

1. Generative Adversarial Networks, Ian J. Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and Bing Xu and David Warde-Farley and Sherjil Ozair and Aaron Courville and Yoshua Bengio, 2014
2. Conditional Generative Adversarial Nets, Mehdi Mirza and Simon Osindero, 2014
3. Image-to-Image Translation with Conditional Adversarial Networks, Phillip Isola and Jun-Yan Zhu and Tinghui Zhou and Alexei A. Efros, 2016
4. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, Jun-Yan Zhu and Taesung Park and Phillip Isola and Alexei A. Efros, 2017
5. Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization, Xun Huang and Serge Belongie, 2017
6. Image Style Transfer Using Convolutional Neural Networks. Gatys, L. A., Ecker, A. S. & Bethge, M. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2414–2423 (IEEE, 2016). doi:10.1109/CVPR.2016.265
7. Inceptionism: Going Deeper into Neural Networks. Alexander Mordvintsev, Mike Tyka