Fetching the paper…
Reading the bibliography…
We present Wasserstein introspective neural networks (WINN) that are both a generator and a discriminator within a single model.
Information and information stability of random variables and processes
M. S. Pinsker · 1960
Earlier work this paper cites.
A learning algorithm for boltzmann machines
D. H. Ackley, G. E. Hinton, and T. J. Sejnowski · 1985
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.
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.
Sparse coding with an overcomplete basis set: A strategy employed by v1?
B. A. Olshausen and D. J. Field · 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.
Self supervised boosting
M. Welling, R. S. Zemel, and G. E. Hinton · 2002
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
G. E. Hinton, S. Osindero, and Y.-W. Teh · 2006
Earlier work this paper cites.
Learning generative models via discriminative approaches
Z. Tu · 2007
Earlier work this paper cites.
A stochastic grammar of images
S.-C. Zhu and D. Mumford · 2007
Earlier work this paper cites.
Learning active basis model for object detection and recognition
Y. N. Wu, Z. Si, H. Gong, and S.-C. Zhu · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Earlier work this paper cites.
Learning deep energy models
J. Ngiam, Z. Chen, P. W. Koh, and A. Y. Ng · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
M. Welling and Y. W. Teh · 2011
Earlier work this paper cites.
Autoencoders, unsupervised learning, and deep architectures
P. Baldi · 2012
Earlier work this paper cites.
ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
Cited alongside, same era.
Learning to generate chairs with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, and T. Brox · 2015
Cited alongside, same era.
Texture synthesis using convolutional neural networks
L. Gatys, A. S. Ecker, and M. Bethge · 2015
Cited alongside, same era.
A neural algorithm of artistic style
L. A. Gatys, A. S. Ecker, and M. Bethge · 2015
Cited alongside, same era.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
Cited alongside, same era.
A theory of generative convnet
J. Xie, Y. Lu, S.-C. Zhu, and Y. N. Wu · 2016
Later among the works it cites.
Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Closest in time.
Adversarially learned inference
V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville · 2017
Closest in time.
Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 2017
Closest in time.
Alternating back-propagation for generator network
T. Han, Y. Lu, S.-C. Zhu, and Y. N. Wu · 2017
Closest in time.
Densely connected convolutional networks
G. Huang*, Z. Liu*, L. van der Maaten, and K. Q. Weinberger · 2017
Closest in time.
Stacked generative adversarial networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
Cited alongside, same era.
Deep convolutional inverse graphics network
T. D. Kulkarni, W. F. Whitney, P. Kohli, and J. Tenenbaum · 2015
Cited alongside, same era.
Deeply-supervised nets
C.-Y. Lee*, S. Xie*, P. Gallagher, Z. Zhang, and Z. Tu · 2015
Cited alongside, same era.
Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
X. Huang, Y. Li, O. Poursaeed, J. Hopcroft, and S. Belongie · 2017
Closest in time.
Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
Closest in time.
Introspective classification with convolutional nets
L. Jin, J. Lazarow, and Z. Tu · 2017
Closest in time.
Adversarial machine learning at scale
A. Kurakin, I. Goodfellow, and S. Bengio · 2017
Closest in time.
Introspective neural networks for generative modeling
J. Lazarow*, L. Jin*, and Z. Tu · 2017
Closest in time.
Approximation and convergence properties of generative adversarial learning
S. Liu, O. Bousquet, and K. Chaudhuri · 2017
Closest in time.
Searching for activation functions
P. Ramachandran, B. Zoph, and Q. V. Le · 2017
Closest in time.
Improving generative adversarial networks with denoising feature matching
D. Warde-Farley and Y. Bengio · 2017
Closest in time.
Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
Closest in time.
Progressive growing of gans for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2018
Closest in time.
Cooperative learning of energy-based model and latent variable model via mcmc teaching
J. Xie, Y. Lu, R. Gao, S.-C. Zhu, and Y. N. Wu · 2018
Closest in time.