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Generative Adversarial Networks (GANs) [Goodfellow et al.
Supervised Descent Method and its Application to Face Alignment
X. Xiong and F. Torre. 2013 · 2013
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Generative Adversarial Neworks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014 · 2014
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Auto-encoding variational bayes
D. P. Kingma and M. Welling. 2014 · 2014
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Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
E. Denton, S. Chintala, A. Szlam, and R. Fergus. 2015 · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy. 2015 · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba. 2015 · 2015
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Deep Learning Face Attributes in the Wild
Z. Liu, P. Luo, X. Wang, and X. Tang. 2015 · 2015
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Generating Images with Perceptual Similarity Metrics based on Deep Networks
A. Dosovitskiy and T. Brox. 2016 · 2016
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Network
A. Radford, L. Metz, and S. Chintala. 2016 · 2016
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Improved techniques for training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Redford, and X. Chen. 2016 · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu. 2016 · 2016
Earlier work this paper cites.
Generative Image Modeling using Style and Structure Adversarial Networks
X. Wang and A. Gupta. 2016 · 2016
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Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
J. Wu, C. Zhang, T. Xue, W. T. Freeman, and J. B. Tenenbaum. 2016 · 2016
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Generative Visual Manipulation on the Natural Image Manifold
J. Zhu, P. Krähenbühl, E. Shechtman, and A. A. Efros. 2016 · 2016
Earlier work this paper cites.
Towards principled methods for training generative adversarial networks
M. Arjovsky and L. Bottou. 2017 · 2017
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Wasserstein GAN
M. Arjovsky, S. Chintala, and L. Bottou. 2017 · 2017
Cited alongside, same era.
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan. 2017 · 2017
Cited alongside, same era.
Photographic Image Synthesis with Cascaded Refinement Networks
Q. Chen and V. Koltun. 2017 · 2017
Cited alongside, same era.
McKernel: A Library for Approximate Kernel Expansions in Log-linear Time
J. D. Curtó, I. C. Zarza, F. Yang, A. Smola, F. Torre, C. W. Ngo, and L. Gool. 2017 · 2017
Cited alongside, same era.
Improved Training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville. 2017 · 2017
Cited alongside, same era.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Data Augmentation Generative Adversarial Networks
A. Antoniou, A. Storkey, and H. Edwards. 2018 · 2018
Closest in time.
Progressive Growing of GANs for Improved Quality, Stability, and Variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen. 2018 · 2018
Closest in time.
Deep Appearance Models for Face Rendering
S. Lombardi, J. Saragih, T. Simon, and Y. Sheikh. 2018 · 2018
Closest in time.
Which Training Methods for GANs do actually Converge?
L. Mescheder, A. Geiger, and S. Nowozin. 2018 · 2018
Closest in time.
FaceShop: Deep Sketch-based Face Image Editing
T. Portenier, Q. Hu, A. Szabó, S. A. Bigdeli, P. Favaro, and M. Zwicker. 2018 · 2018
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SMIT: Stochastic Multi-Label Image-to-Image Translation
A. Romero, P. Arbeláez, L. Gool, and R. Timofte. 2018 · 2018
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M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter. 2017 · 2017
Cited alongside, same era.
Self-Normalizing Neural Networks
G. Klarbauer, T. Unterthiner, and A. Mayr. 2017 · 2017
Cited alongside, same era.
Triple Generative Adversarial Nets
C. Li, K. Xu, J. Zhu, and B. Zhang. 2017 · 2017
Cited alongside, same era.
The Numerics of GANs
L. Mescheder, S. Nowozin, and A. Geiger. 2017 · 2017
Cited alongside, same era.
Conditional Image Synthesis With Auxiliary Classifier GANs
A. Odena, C. Olah, and J. Shlens. 2017 · 2017
Cited alongside, same era.
Amortised map inference for image super-resolution
C. K. Sønderby, J. Caballero, L. Theis, W. Shi, and F. Hussar. 2017 · 2017
Cited alongside, same era.
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
C. Yang, X. Lu, Z. Lin, E. Shechtman, O. Wang, and H. Li. 2017 · 2017
Cited alongside, same era.
Closest in time.
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
S. Sankaranarayanan, Y. Balaji, A. Jain, S. Lim, and R. Chellappa. 2018 · 2018
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Video-to-Video Synthesis
T. Wang, M. Liu, J. Zhu, G. Liu, A. Tao, J. Kautz, and B. Catanzaro. 2018a · 2018
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High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
T. Wang, M. Liu, J. Zhu, A. Tao, J. Kautz, and B. Catanzaro. 2018b · 2018
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Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
X. Wei, B. Gong, Z. Liu, W. Lu, and L. Wang. 2018 · 2018
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Wasserstein Divergence for GANs
J. Wu, Z. Huang, J. Thoma, D. Acharya, and L. Gool. 2018 · 2018
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Generative Image Inpainting with Contextual Attention
J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang. 2018 · 2018
Closest in time.
Large Scale GAN Training for High Fidelity Natural Image Synthesis
A. Brock, J. Donahue, and K. Simonyan. 2019 · 2019
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Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach
Y. Chen, W. Li, X. Chen, and L. Gool. 2019 · 2019
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