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Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data.
Best practices for convolutional neural networks applied to visual document analysis
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller · 2007
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hintion · 2012
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean · 2013
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Face recognition based on regularized nearest points between image sets
M. Yang, P. Zhu, L. Van Gool, and L. Zhang · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Semi-supervised learning with deep generative models
D. P. Kingma, S. Mohamed, D. Jimenez Rezende, and M. Welling · 2014
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
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Deeppose: Human pose estimation via deep neural networks
A. Toshev and C. Szegedy · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Deep convolutional inverse graphics network
T. D. Kulkarni, W. F. Whitney, P. Kohli, and J. Tenenbaum · 2015
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Age and gender classification using convolutional neural networks
G. Levi and T. Hassner · 2015
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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Deep face recognition
O. M. Parkhi, A. Vedaldi, and A. Zisserman · 2015
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Deep face recongnition
O. M. Parkhi, A. Vedaldi, and A. Zisserman · 2015
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Very deep convolutional networks for large-scale image recongnition
K. Simonyan and A. Zisserman · 2015
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Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views
H. Su, C. R. Qi, Y. Li, and L. J. Guibas · 2015
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Hyper-class augmented and regularized deep learning for fine-grained image classification
S. Xie, T. Yang, X. Wang, and Y. Lin · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
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Ms-celeb-1m: A dataset and benchmark for large scale face recognition
Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao · 2016
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Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
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Adversarial autoencoders
A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey · 2016
Learning to discover cross-domain relations with generative adversarial networks
T. Kim, M. Cha, H. Kim, J. K. Lee, and J. Kim · 2017
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Unsupervised visual attribute transfer with reconfigurable generative adversarial networks
T. Kim, B. Kim, M. Cha, and J. Kim · 2017
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Generative semantic manipulation with contrasting gan
X. Liang, H. Zhang, and E. P. Xing · 2017
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Unsupervised image-to-image translation networks
M.-Y. Liu, T. Breuel, and J. Kautz · 2017
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Conditional cyclegan for attribute guided face image generation
Y. Lu, Y.-W. Tai, and C.-K. Tang · 2017
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Conditional image synthesis with auxiliary classifier gans
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Invertible conditional gans for image editing
G. Perarnau, J. van de Weijer, B. Raducanu, and J. M. Álvarez · 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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A discriminative feature learning approach for deep face recognition
Y. Wen, K. Zhang, Z. Li, and Y. Qiao · 2016
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Attribute2image: Conditional image generation from visual attributes
X. Yan, J. Yang, K. Sohn, and H. Lee · 2016
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Unsupervised pixel-level domain adaption with generative adversarial networks
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan · 2017
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Adversarially learned inference
V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville · 2017
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A. Odena, C. Olah, and J. Shlens · 2017
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Learning from simulated and unsupervised images through adversarial training
A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb · 2017
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Rendergan: Generating realistic labeled data
L. Sixt, B. Wild, and T. Landgraf · 2017
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Unsupervised cross-domain image generation
Y. Taigman, A. Polyak, and L. Wolf · 2017
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Dualgan: Unsupervised dual learning for image-to-image translation
Z. Yi, H. Zhang, P. Tan, and M. Gong · 2017
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Image de-raining using a conditional generative adversarial network
H. Zhang, V. Sindagi, and V. M. Patel · 2017
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Age progression/regression by conditional adversarial autoencoder
Z. Zhang, Y. Song, and H. Qi · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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A systematic evaluation and benchmark for person re-identification: Features, metrics, and datasets
S. Karanam, M. Gou, Z. Wu, A. Rates-Borras, O. Camps, and R. J. Radke · 2018
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Tell me where to look: Guided attention inference network
K. Li, Z. Wu, K.-C. Peng, J. Ernst, and Y. Fu · 2018
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