Fetching the paper…
Reading the bibliography…
Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
Earlier work this paper cites.
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani · 2006
Earlier work this paper cites.
Deep learning via semi-supervised embedding
Jason Weston, Frédéric Ratle, Hossein Mobahi, and Ronan Collobert · 2012
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.
Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
Earlier work this paper cites.
Distributional smoothing with virtual adversarial training
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii · 2015
Earlier work this paper cites.
Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
Earlier work this paper cites.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
Jost Tobias Springenberg · 2015
Earlier work this paper cites.
Efficient object localization using convolutional networks
Jonathan Tompson, Ross Goroshin, Arjun Jain, Yann LeCun, and Christoph Bregler · 2015
Cited alongside, same era.
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
Cited alongside, same era.
Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, and Aaron Courville · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
Energy-based generative adversarial network
Junbo Zhao, Michael Mathieu, and Yann LeCun · 2016
Later among the works it cites.
Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
Closest in time.
Calibrating energy-based generative adversarial networks
Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard Hovy, and Aaron Courville · 2017
Closest in time.
Triple generative adversarial nets
Chongxuan Li, Kun Xu, Jun Zhu, and Bo Zhang · 2017
Closest in time.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Auxiliary deep generative models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther · 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.
Amortised map inference for image super-resolution
Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, and Ferenc Huszár · 2016
Cited alongside, same era.
Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov · 2016
Cited alongside, same era.
Closest in time.
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
Closest in time.
Adversarial generator-encoder networks
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2017
Closest in time.
Semi-supervised qa with generative domain-adaptive nets
Zhilin Yang, Junjie Hu, Ruslan Salakhutdinov, and William W Cohen · 2017
Closest in time.