Fetching the paper…
Reading the bibliography…
Traditional generative adversarial networks (GAN) and many of its variants are trained by minimizing the KL or JS-divergence loss that measures how close the generated data distribution is from the true data distribution.
Overview of the face recognition grand challenge
P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek · 2005
Earlier work this paper cites.
Optimal transport: old and new
C. Villani · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Multi-pie
R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker · 2010
Earlier work this paper cites.
Distinguishing identical twins by face recognition
P. J. Phillips, P. J. Flynn, K. W. Bowyer, R. W. V. Bruegge, P. J. Grother, G. W. Quinn, and M. Pruitt · 2011
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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.
Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
Earlier work this paper cites.
Deep generative image models using a laplacian pyramid of adversarial networks
E. L. Denton, S. Chintala, R. Fergus, et al · 2015
Earlier work this paper cites.
Draw: A recurrent neural network for image generation
K. Gregor, I. Danihelka, A. Graves, D. J. Rezende, and D. Wierstra · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao · 2015
Cited alongside, same era.
Openface: A general-purpose face recognition library with mobile applications
B. Amos, L. Bartosz, and M. Satyanarayanan · 2016
Cited alongside, same era.
Neural photo editing with introspective adversarial networks
A. Brock, T. Lim, J. Ritchie, and N. Weston · 2016
Cited alongside, same era.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
Cited alongside, same era.
Conditional image synthesis with auxiliary classifier gans
A. Odena, C. Olah, and J. Shlens · 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.
Unsupervised cross-domain image generation
Y. Taigman, A. Polyak, and L. Wolf · 2016
Later among the works it cites.
Pixel recurrent neural networks
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
Later among the works it cites.
Conditional image generation with pixelcnn decoders
A. van den Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves, and K. Kavukcuoglu · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Donahue, P. Krähenbühl, and T. Darrell · 2016
Cited alongside, same era.
Adversarially learned inference
V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky, O. Mastropietro, and A. Courville · 2016
Cited alongside, same era.
Generative multi-adversarial networks
I. Durugkar, I. Gemp, and S. Mahadevan · 2016
Cited alongside, same era.
Stacked generative adversarial networks
X. Huang, Y. Li, O. Poursaeed, J. Hopcroft, and S. Belongie · 2016
Cited alongside, same era.
Generating images with recurrent adversarial networks
D. J. Im, C. D. Kim, H. Jiang, and R. Memisevic · 2016
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2016
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2016
Cited alongside, same era.
Plug & play generative networks: Conditional iterative generation of images in latent space
A. Nguyen, J. Yosinski, Y. Bengio, A. Dosovitskiy, and J. Clune · 2016
Cited alongside, same era.
Later among the works it cites.
Semantic image inpainting with perceptual and contextual losses
R. Yeh, C. Chen, T. Y. Lim, M. Hasegawa-Johnson, and M. N. Do · 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.
Towards principled methods for training generative adversarial networks
M. Arjovsky and L. Bottou · 2017
Closest in time.
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Closest in time.
Boundary-seeking generative adversarial networks
R. D. Hjelm, A. P. Jacob, T. Che, K. Cho, and Y. Bengio · 2017
Closest in time.
Least squares generative adversarial networks
X. Mao, Q. Li, H. Xie, R. Y. Lau, and Z. Wang · 2017
Closest in time.
Lr-gan - layered recursive generative adversarial networks for image generation
J. Yang, A. Kannan, B. Batra, and D. Parikh · 2017
Closest in time.