Must-Read Papers on Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs) are one of the most popular techniques in deep learning. GANs use two neural networks to generate new, synthetic instances of data that can pass for real data. This graph maps the most important papers and their video summaries derived from the original GANs proposed by Ian Goodfellow.
Other recommended papers
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida
Yanghua Jin, Jiakai Zhang, Minjun Li, Yingtao Tian, Huachun Zhu, Zhihao Fang
David Berthelot, Thomas Schumm, Luke Metz
Mehdi Mirza, Simon Osindero