Fetching the paper…
Reading the bibliography…
This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that learns an interpretable representation of images.
Convolutional networks for images, speech, and time series
Y. LeCun and Y. Bengio · 1995
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
G. Hinton, S. Osindero, and Y.-W. Teh · 2006
Earlier work this paper cites.
Unsupervised learning of invariant feature hierarchies with applications to object recognition
M. Ranzato, F. J. Huang, Y.-L. Boureau, and Y. LeCun · 2007
Earlier work this paper cites.
Analysis-by-synthesis by learning to invert generative black boxes
V. Nair, J. Susskind, and G. E. Hinton · 2008
Earlier work this paper cites.
Learning deep architectures for ai
Y. Bengio · 2009
Earlier work this paper cites.
Measuring invariances in deep networks
I. Goodfellow, H. Lee, Q. V. Le, A. Saxe, and A. Y. Ng · 2009
Earlier work this paper cites.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng · 2009
Earlier work this paper cites.
A 3d face model for pose and illumination invariant face recognition
P. Paysan, R. Knothe, B. Amberg, S. Romdhani, and T. Vetter · 2009
Earlier work this paper cites.
Deep boltzmann machines
R. Salakhutdinov and G. E. Hinton · 2009
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol · 2010
Cited alongside, same era.
Transforming auto-encoders
G. E. Hinton, A. Krizhevsky, and S. D. Wang · 2011
Cited alongside, same era.
Disentangling factors of variation via generative entangling
G. Desjardins, A. Courville, and Y. Bengio · 2012
Cited alongside, same era.
Y. Tang, R. Salakhutdinov, and G. Hinton · 2012
Cited alongside, same era.
Lecture 6.5 - rmsprop, coursera: Neural networks for machine learning
T. Tieleman and G. Hinton · 2012
Cited alongside, same era.
Learning the irreducible representations of commutative lie groups
T. Cohen and M. Welling · 2014
Later among the works it cites.
V. Jampani, S. Nowozin, M. Loper, and P. V. Gehler · 2014
Later among the works it cites.
Inverse graphics with probabilistic cad models
T. D. Kulkarni, V. K. Mansinghka, P. Kohli, and J. B. Tenenbaum · 2014
Later among the works it cites.
Variational particle approximations
T. D. Kulkarni, A. Saeedi, and S. Gershman · 2014
Later among the works it cites.
Opendr: An approximate differentiable renderer
M. M. Loper and M. J. Black · 2014
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
Cited alongside, same era.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Cited alongside, same era.
Approximate bayesian image interpretation using generative probabilistic graphics programs
V. Mansinghka, T. D. Kulkarni, Y. N. Perov, and J. Tenenbaum · 2013
Cited alongside, same era.
Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models
M. Aubry, D. Maturana, A. Efros, B. Russell, and J. Sivic · 2014
Cited alongside, same era.
Later among the works it cites.
The role of context for object detection and semantic segmentation in the wild
R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille · 2014
Later among the works it cites.
Optimizing Neural Networks that Generate Images
T. Tieleman · 2014
Later among the works it cites.
Learning to generate chairs with convolutional neural networks
A. Dosovitskiy, J. Springenberg, and T. Brox · 2015
Closest in time.