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Deep generative models (DGMs) have shown promise in image generation.
Long short-term memory
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Gradient-based learning applied to document recognition
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Graphical models, exponential families, and variational inference
Martin J Wainwright, Michael I Jordan, et al · 2008
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Posterior regularization for structured latent variable models
Kuzman Ganchev, Jennifer Gillenwater, Ben Taskar, et al · 2010
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Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Weakly supervised object detection with posterior regularization
Hakan Bilen, Marco Pedersoli, and Tinne Tuytelaars · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Graphical generative adversarial networks
Chongxuan Li, Max Welling, Jun Zhu, and Bo Zhang · 2018
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Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Triple generative adversarial nets
LI Chongxuan, Taufik Xu, Jun Zhu, and Bo Zhang · 2017
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Amortized inference regularization
Rui Shu, Hung H Bui, Shengjia Zhao, Mykel J Kochenderfer, and Stefano Ermon · 2018
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Deep structured generative models
Kun Xu, Haoyu Liang, Jun Zhu, Hang Su, and Bo Zhang · 2018
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Monet: Unsupervised scene decomposition and representation
Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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Multi-object representation learning with iterative variational inference
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