Fetching the paper…
Reading the bibliography…
Previous works on sequential learning address the problem of forgetting in discriminative models.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J. Cohen · 1989
Earlier work this paper cites.
Catastrophic forgetting, rehearsal and pseudorehearsal
Anthony Robins · 1995
Earlier work this paper cites.
The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Sleep transforms the cerebral trace of declarative memories
Steffen Gais et al · 2007
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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 P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2016
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
Cited alongside, same era.
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley · 2017
Later among the works it cites.
Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
Later among the works it cites.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Cristoph H. Lampert · 2017
Later among the works it cites.
Continual learning in generative adversarial nets
Ari Seff, Alex Beatson, Daniel Suo, and Han Liu · 2017
Later among the works it cites.
Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
Later among the works it cites.
Progressive growing of gans for improved quality, stability, and variation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A learned representation for artistic style
Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick et al · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, and Sepp Hochreiter · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
David Lopez-Paz et al · 2017
Cited alongside, same era.
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Closest in time.
Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2018
Closest in time.
Rotate your networks: Better weight consolidation and less catastrophic forgetting
Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M Lopez, and Andrew D Bagdanov · 2018
Closest in time.
Cgans with projection discriminator
Takeru Miyato and Masanori Koyama · 2018
Closest in time.
Transferring GANs: generating images from limited data
Yaxing Wang, Chenshen Wu, Luis Herranz, Joost van de Weijer, Abel Gonzalez-Garcia, and Bogdan Raducanu · 2018
Closest in time.