Boundary-Seeking Generative Adversarial Networks
R Devon Hjelm, Athul Paul Jacob, Tong Che, Adam Trischler, Kyunghyun Cho, Yoshua Bengio
https://arxiv.org/abs/1702.08431
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, Jiwon Kim
https://arxiv.org/abs/1703.05192
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
Zili Yi, Hao Zhang, Ping Tan, Minglun Gong
https://arxiv.org/abs/1704.02510
GAN
对,就是Ian Goodfellow那个原版GAN。
Paper:
Generative Adversarial Networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
https://arxiv.org/abs/1406.2661
Least Squares Generative Adversarial Networks
Xudong Mao, Qing Li, Haoran Xie, Raymond Y.K. Lau, Zhen Wang, Stephen Paul Smolley
https://arxiv.org/abs/1611.04076
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan
https://arxiv.org/abs/1612.05424
Semi-Supervised GAN
半监督生成对抗网络简称SGAN。它通过强制让辨别器输出类别标签,实现了GAN在半监督环境下的训练。
Paper:
Semi-Supervised Learning with Generative Adversarial NetworksAugustus Odenahttps://arxiv.org/abs/1606.01583
Super-Resolution GAN
超分辨率生成对抗网络简称SRGAN,将GAN用到了超分辨率任务上,可以将照片扩大4倍。
Paper:
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi
https://arxiv.org/abs/1609.04802
Wasserstein GAN
Martin Arjovsky, Soumith Chintala, Léon Bottou
https://arxiv.org/abs/1701.07875
Wasserstein GAN GP
WGAN的改进版。
Paper:
Improved Training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville
https://arxiv.org/abs/1704.00028