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Generative Adversarial Networks (GANs) have recently demonstrated the capability to synthesize compelling real-world images, such as room interiors, album covers, manga, faces, birds, and flowers.
Deep boltzmann machines
R. Salakhutdinov and G. E. Hinton · 2009
Earlier work this paper cites.
The neural autoregressive distribution estimator
H. Larochelle and I. Murray · 2011
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Earlier work this paper cites.
2d human pose estimation: New benchmark and state of the art analysis
M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele · 2014
Earlier work this paper cites.
On the properties of neural machine translation: Encoder–decoder approaches
K. Cho, B. van Merriënboer, D. Bahdanau, 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
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
Earlier work this paper cites.
Unifying visual-semantic embeddings with multimodal neural language models
R. Kiros, R. Salakhutdinov, and R. S. Zemel · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
Earlier work this paper cites.
Evaluation of Output Embeddings for Fine-Grained Image Classification
Z. Akata, S. Reed, D. Walter, H. Lee, and B. Schiele · 2015
Cited alongside, same era.
Deep generative image models using a laplacian pyramid of adversarial networks
E. L. Denton, S. Chintala, R. Fergus, et al · 2015
Cited alongside, same era.
Learning to generate chairs with convolutional neural networks
A. Dosovitskiy, J. Tobias Springenberg, and T. Brox · 2015
Cited alongside, same era.
Draw: A recurrent neural network for image generation
K. Gregor, I. Danihelka, A. Graves, D. Rezende, and D. Wierstra · 2015
Cited alongside, same era.
Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 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.
Generative image modeling using spatial lstms
L. Theis and M. Bethge · 2015
Later among the works it cites.
Weakly-supervised disentangling with recurrent transformations for 3d view synthesis
J. Yang, S. Reed, M.-H. Yang, and H. Lee · 2015
Later among the works it cites.
Attend, infer, repeat: Fast scene understanding with generative models
S. Eslami, N. Heess, T. Weber, Y. Tassa, K. Kavukcuoglu, and G. E. Hinton · 2016
Closest in time.
Generating images from captions with attention
E. Mansimov, E. Parisotto, J. L. Ba, and R. Salakhutdinov · 2016
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Modules for spatial transformer networks
Q. Oquab · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
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Action-conditional video prediction using deep networks in atari games
J. Oh, X. Guo, H. Lee, R. L. Lewis, and S. Singh · 2015
Cited alongside, same era.
Deep visual analogy-making
S. Reed, Y. Zhang, Y. Zhang, and H. Lee · 2015
Cited alongside, same era.
Learning deep representations for fine-grained visual descriptions
S. Reed, Z. Akata, H. Lee, and B. Schiele
Cited in the paper.
Generative adversarial text-to-image synthesis
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee
Cited in the paper.
Closest in time.
One-shot generalization in deep generative models
D. J. Rezende, S. Mohamed, I. Danihelka, K. Gregor, and D. Wierstra · 2016
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Pixel recurrent neural networks
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
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