Fetching the paper…
Reading the bibliography…
We study unsupervised learning by developing introspective generative modeling (IGM) that attains a generator using progressively learned deep convolutional neural networks.
Modeling by shortest data description
J. Rissanen · 1978
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel · 1989
Earlier work this paper cites.
Feature extraction from faces using deformable templates
A. L. Yuille, P. W. Hallinan, and D. S. Cohen · 1992
Earlier work this paper cites.
General pattern theory-A mathematical study of regular structures
U. Grenander · 1993
Earlier work this paper cites.
Pyramid-based texture analysis/synthesis
D. J. Heeger and J. R. Bergen · 1995
Earlier work this paper cites.
The” wake-sleep” algorithm for unsupervised neural networks
G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal · 1995
Earlier work this paper cites.
The nature of statistical learning theory
V. N. Vapnik · 1995
Earlier work this paper cites.
Inducing features of random fields
S. Della Pietra, V. Della Pietra, and J. Lafferty · 1997
Earlier work this paper cites.
A decision-theoretic generalization of on-line learning and an application to boosting
Y. Freund and R. E. Schapire · 1997
Earlier work this paper cites.
Minimax entropy principle and its application to texture modeling
S. C. Zhu, Y. N. Wu, and D. Mumford · 1997
Earlier work this paper cites.
Texture synthesis by non-parametric sampling
A. A. Efros and T. K. Leung · 1999
Earlier work this paper cites.
Pattern Classification
R. O. Duda, P. E. Hart, and D. G. Stork · 2000
Earlier work this paper cites.
A parametric texture model based on joint statistics of complex wavelet coefficients
J. Portilla and E. P. Simoncelli · 2000
Earlier work this paper cites.
Normalized cuts and image segmentation
J. Shi and J. Malik · 2000
Earlier work this paper cites.
Equivalence of julesz ensembles and frame models
Y. N. Wu, S. C. Zhu, and X. Liu · 2000
Earlier work this paper cites.
Random Forests
L. Breiman · 2001
Earlier work this paper cites.
The elements of statistical learning, volume 1
J. Friedman, T. Hastie, and R. Tibshirani · 2001
Earlier work this paper cites.
Principal component analysis
I. Jolliffe · 2002
Cited alongside, same era.
Self supervised boosting
M. Welling, R. S. Zemel, and G. E. Hinton · 2002
Cited alongside, same era.
Latent dirichlet allocation
D. M. Blei, A. Y. Ng, and M. I. Jordan · 2003
Cited alongside, same era.
On contrastive divergence learning
M. A. Carreira-Perpinan and G. Hinton · 2005
Cited alongside, same era.
Fields of experts: A framework for learning image priors
S. Roth and M. J. Black · 2005
Cited alongside, same era.
A fast learning algorithm for deep belief nets
G. E. Hinton, S. Osindero, and Y. W. Teh · 2006
Cited alongside, same era.
Vision as bayesian inference: analysis by synthesis?
A. L. Yuille and D. Kersten · 2006
Stochastic gradient hamiltonian monte carlo
T. Chen, E. B. Fox, and C. Guestrin · 2014
Later among the works it cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Later among the works it cites.
A neural algorithm of artistic style
L. A. Gatys, A. S. Ecker, and M. Bethge · 2015
Later among the works it cites.
Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
Later among the works it cites.
Deepdream - a code example for visualizing neural networks
A. Mordvintsev, C. Olah, and M. Tyka · 2015
Later among the works it cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Scaling learning algorithms towards ai
Y. Bengio and Y. LeCun · 2007
Cited alongside, same era.
Learning generative models via discriminative approaches
Z. Tu · 2007
Cited alongside, same era.
An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators
P. Liang and M. I. Jordan · 2008
Cited alongside, same era.
Monte Carlo strategies in scientific computing
J. S. Liu · 2008
Cited alongside, same era.
Brain anatomical structure segmentation by hybrid discriminative/generative models
Z. Tu, K. L. Narr, P. Dollár, I. Dinov, P. M. Thompson, and A. W. Toga · 2008
Cited alongside, same era.
Later among the works it cites.
Neural photo editing with introspective adversarial networks
A. Brock, T. Lim, J. Ritchie, and N. Weston · 2016
Later among the works it cites.
DCGAN-tensorflow
T. Kim · 2016
Later among the works it cites.
Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Later among the works it cites.
Texture networks: Feed-forward synthesis of textures and stylized images
D. Ulyanov, V. Lebedev, A. Vedaldi, and V. Lempitsky · 2016
Later among the works it cites.
Cooperative training of descriptor and generator networks
J. Xie, Y. Lu, S.-C. Zhu, and Y. N. Wu · 2016
Later among the works it cites.
A theory of generative convnet
J. Xie, Y. Lu, S.-C. Zhu, and Y. N. Wu · 2016
Later among the works it cites.
Energy-based generative adversarial network
J. Zhao, M. Mathieu, and Y. LeCun · 2016
Later among the works it cites.
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Closest in time.
Introspective classifier learning: Empower generatively
L. Jin, J. Lazarow, and Z. Tu · 2017
Closest in time.
Stochastic gradient descent as approximate bayesian inference
S. Mandt, M. D. Hoffman, and D. M. Blei · 2017
Closest in time.
Adagan: Boosting generative models
I. Tolstikhin, S. Gelly, O. Bousquet, C.-J. Simon-Gabriel, and B. Schölkopf · 2017
Closest in time.