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Building on top of the success of generative adversarial networks (GANs), conditional GANs attempt to better direct the data generation process by conditioning with certain additional information.
“The mnist database of handwritten digits,”
Yann LeCun, · 1998
Earlier work this paper cites.
“Generative adversarial nets,”
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, · 2014
Earlier work this paper cites.
“Conditional generative adversarial nets,”
Mehdi Mirza and Simon Osindero, · 2014
Earlier work this paper cites.
“Adam: A method for stochastic optimization,”
Diederik Kingma and Jimmy Ba, · 2014
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“Iris recognition based on a novel variation of local binary pattern,”
Chengcheng Li, Weidong Zhou, and Shasha Yuan, · 2015
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“Deep learning face attributes in the wild,”
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang, · 2015
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“Unsupervised representation learning with deep convolutional generative adversarial networks,”
Alec Radford, Luke Metz, and Soumith Chintala, · 2015
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“A note on the evaluation of generative models,”
Lucas Theis, Aäron van den Oord, and Matthias Bethge, · 2015
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“Neural photo editing with introspective adversarial networks,”
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston, · 2016
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“Tutorial on variational autoencoders,”
Carl Doersch, · 2016
Cited alongside, same era.
“Pixel recurrent neural networks,”
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu, · 2016
Cited alongside, same era.
“Conditional image generation with pixelcnn decoders,”
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al., · 2016
Cited alongside, same era.
“Image-to-image translation with conditional adversarial networks,”
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros, · 2016
Cited alongside, same era.
“Learning what and where to draw,”
Scott E Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, and Honglak Lee, · 2016
Cited alongside, same era.
“Rethinking the inception architecture for computer vision,”
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna, · 2016
Later among the works it cites.
“Semantic image inpainting with deep generative models,”
Raymond A Yeh, Chen Chen, Teck Yian Lim, Alexander G Schwing, Mark Hasegawa-Johnson, and Minh N Do, · 2017
Later among the works it cites.
“Age progression/regression by conditional adversarial autoencoder,”
Zhifei Zhang, Yang Song, and Hairong Qi, · 2017
Later among the works it cites.
“Image-to-image translation with conditional adversarial networks,”
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros, · 2017
Later among the works it cites.
“Pose guided person image generation,”
Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, and Luc Van Gool, · 2017
Later among the works it cites.
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Hanock Kwak and Byoung-Tak Zhang, · 2016
Cited alongside, same era.
“Conditional image synthesis with auxiliary classifier gans,”
Augustus Odena, Christopher Olah, and Jonathon Shlens, · 2016
Cited alongside, same era.
“Improved techniques for training gans,”
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen, · 2016
Cited alongside, same era.
“keras-acgane: Auxiliary classifier generative adversarial networks in keras,” https://github.com/lukedeo/keras-acgan, 2017
Luke de Oliveira, · 2017
Later among the works it cites.
“Semi-supervised fusedgan for conditional image generation,”
Navaneeth Bodla, Gang Hua, and Rama Chellappa, · 2018
Closest in time.
“Deep reinforcement learning of cell movement in the early stage of c. elegans embryogenesis,”
Zi Wang, Dali Wang, Chengcheng Li, Yichi Xu, Husheng Li, and Zhirong Bao, · 2018
Closest in time.