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We present a generative model of images based on layering, in which image layers are individually generated, then composited from front to back.
Maximum likelihood from incomplete data via the em algorithm
Dempster, Arthur P, Laird, Nan M, and Rubin, Donald B · 1977
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Compositing digital images
Porter, Thomas and Duff, Tom · 1984
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Robust estimation of a multi-layered motion representation
Darrell, Trevor and Pentland, Alex · 1991
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Representing moving images with layers
Wang, John YA and Adelson, Edward H · 1994
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Layered representation of motion video using robust maximum-likelihood estimation of mixture models and mdl encoding
Ayer, Serge and Sawhney, Harpreet S · 1995
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Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
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View from the top: hierarchies and reverse hierarchies in the visual system
Hochstein, Shaul and Ahissar, Merav · 2002
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Greedy learning of multiple objects in images using robust statistics and factorial learning
Williams, Christopher KI and Titsias, Michalis K · 2004
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Generative model for layers of appearance and deformation
Kannan, Anitha, Jojic, Nebojsa, and Frey, B · 2005
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Fast transformation-invariant component analysis
Kannan, Anitha, Jojic, Nebojsa, and Frey, Brendan J · 2008
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Graphical models, exponential families, and variational inference
Wainwright, Martin J and Jordan, Michael I · 2008
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Understanding the difficulty of training deep feedforward neural networks
Glorot, Xavier and Bengio, Yoshua · 2010
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Transforming auto-encoders
Hinton, Geoffrey E, Krizhevsky, Alex, and Wang, Sida D · 2011
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Learning a generative model of images by factoring appearance and shape
Le Roux, Nicolas, Heess, Nicolas, Shotton, Jamie, and Winn, John · 2011
Cited alongside, same era.
Layered object models for image segmentation
Yang, Yi, Hallman, Sam, Ramanan, Deva, and Fowlkes, Charless C · 2012
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Scene collaging: Analysis and synthesis of natural images with semantic layers
Semi-supervised learning with deep generative models
Kingma, Diederik P, Mohamed, Shakir, Rezende, Danilo Jimenez, and Welling, Max · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan · 2014
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Importance weighted autoencoders
Burda, Yuri, Grosse, Roger, and Salakhutdinov, Ruslan · 2015
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Deep generative image models using a laplacian pyramid of adversarial networks
Denton, Emily, Chintala, Soumith, Szlam, Arthur, and Fergus, Rob · 2015
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Learning to generate chairs with convolutional neural networks
Dosovitskiy, Alexey, Springenberg, Jost Tobias, and Brox, Thomas · 2015
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Isola, P and Liu, Ce · 2013
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Discovering hidden factors of variation in deep networks
Cheung, Brian, Livezey, Jesse A, Bansal, Arjun K, and Olshausen, Bruno A · 2014
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Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
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Simultaneous detection and segmentation
Hariharan, Bharath, Arbel, Pablo, Girshick, Ross, and Malik, Jitendra · 2014
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Auto-encoding variational bayes
Kingma, Diederik P. and Welling, Max · 2014
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Draw: A recurrent neural network for image generation
Gregor, Karol, Danihelka, Ivo, Graves, Alex, and Wierstra, Daan · 2015
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Jaderberg, Max, Simonyan, Karen, Zisserman, Andrew, and Kavukcuoglu, Koray · 2015
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Amodal completion and size constancy in natural scenes
Kar, A., Tulsiani, S., Carreira, J., and Malik, J · 2015
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Deep convolutional inverse graphics network
Kulkarni, Tejas D, Whitney, Will, Kohli, Pushmeet, and Tenenbaum, Joshua B · 2015
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