Fetching the paper…
Reading the bibliography…
Deep generative models seek to recover the process with which the observed data was generated.
Comparing partitions
Lawrence Hubert and Phipps Arabie · 1985
Earlier work this paper cites.
Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments
J Schmidhuber · 1990
Earlier work this paper cites.
Learning factorial codes by predictability minimization
Jürgen Schmidhuber · 1992
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
Earlier work this paper cites.
Core knowledge
Elizabeth S Spelke and Katherine D Kinzler · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Learning a generative model of images by factoring appearance and shape
Nicolas Le Roux, Nicolas Heess, Jamie Shotton, and John Winn · 2011
Earlier work this paper cites.
Simulation as an engine of physical scene understanding
Peter W Battaglia, Jessica B Hamrick, and Joshua B Tenenbaum · 2013
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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.
Stochastic gradient vb and the variational auto-encoder
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Draw: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, and Daan Wierstra · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Attend, infer, repeat: Fast scene understanding with generative models
SM Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Geoffrey E Hinton, et al · 2016
Earlier work this paper cites.
Tagger: Deep unsupervised perceptual grouping
Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hao, Harri Valpola, and Juergen Schmidhuber · 2016
Earlier work this paper cites.
Generating images with recurrent adversarial networks
Daniel Jiwoong Im, Chris Dongjoo Kim, Hui Jiang, and Roland Memisevic · 2016
Cited alongside, same era.
Generating images part by part with composite generative adversarial networks
Hanock Kwak and Byoung-Tak Zhang · 2016
Cited alongside, same era.
Pixel recurrent neural networks
Aaron Van Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Cited alongside, same era.
Adversarial feature learning
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2017
Image generation from scene graphs
Justin Johnson, Agrim Gupta, and Li Fei-Fei · 2018
Closest in time.
Sequential attend, infer, repeat: Generative modelling of moving objects
Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, and Ingmar Posner · 2018
Closest in time.
St-gan: Spatial transformer generative adversarial networks for image compositing
Chen-Hsuan Lin, Ersin Yumer, Oliver Wang, Eli Shechtman, and Simon Lucey · 2018
Closest in time.
Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2018
Closest in time.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Closest in time.
Deep structured generative models
Kun Xu, Haoyu Liang, Jun Zhu, Hang Su, and Bo Zhang · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2017
Cited alongside, same era.
Neural expectation maximization
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 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 local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Cited alongside, same era.
Closest in time.
Object discovery with a copy-pasting gan
Relja Arandjelović and Andrew Zisserman · 2019
Closest in time.
Compositional gan: Learning image-conditional binary composition
Samaneh Azadi, Deepak Pathak, Sayna Ebrahimi, and Trevor Darrell · 2019
Closest in time.
Emergence of object segmentation in perturbed generative models
Adam Bielski and Paolo Favaro · 2019
Closest in time.
Unsupervised object segmentation by redrawing
Mickaël Chen, Thierry Artières, and Ludovic Denoyer · 2019
Closest in time.
Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
Closest in time.
Generating multiple objects at spatially distinct locations
Tobias Hinz, Stefan Heinrich, and Stefan Wermter · 2019
Closest in time.
Reasoning about physical interactions with object-centric models
Michael Janner, Sergey Levine, William T. Freeman, Joshua B. Tenenbaum, Chelsea Finn, and Jiajun Wu · 2019
Closest in time.
A large-scale study on regularization and normalization in gans
Karol Kurach, Mario Lučić, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly · 2019
Closest in time.
Adversarial framework for unsupervised learning of motion dynamics in videos
C Spampinato, S Palazzo, P D’Oro, D Giordano, and M Shah · 2019
Closest in time.
Deep reinforcement learning with relational inductive biases
Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew Botvinick, Oriol Vinyals, and Peter Battaglia · 2019
Closest in time.
Generative adversarial networks are special cases of artificial curiosity (1990) and also closely related to predictability minimization (1991)
Jürgen Schmidhuber · 2020
Closest in time.