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Learning to generate natural scenes has always been a challenging task in computer vision.
Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1
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T.-Y. Lin, S. Belongie, and J. Hays · 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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Conditional generative adversarial nets
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Action recognition in the presence of one egocentric and multiple static cameras
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Learning deep representations for ground-to-aerial geolocalization
T. Lin, Y. Cui, S. J. Belongie, and J. Hays · 2015
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Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2015
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S. Workman, R. Souvenir, and N. Jacobs · 2015
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Ego2top: Matching viewers in egocentric and top-view videos
S. Ardeshir and A. Borji · 2016
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Egotransfer: Transferring motion across egocentric and exocentric domains using deep neural networks
S. Ardeshir, K. Regmi, and A. Borji · 2016
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Mode regularized generative adversarial networks
T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li · 2016
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The cityscapes dataset for semantic urban scene understanding
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Learning to generate chairs, tables and cars with convolutional networks
A. Dosovitskiy, J. T. Springenberg, M. Tatarchenko, and T. Brox · 2017
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Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
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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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A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2017
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Generative adversarial text to image synthesis
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee · 2016
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Improved techniques for training gans
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Multi-view 3d models from single images with a convolutional network
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2016
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A note on the evaluation of generative models
L. Theis, A. van den Oord, and M. Bethge · 2016
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Localizing and Orienting Street Views Using Overhead Imagery
N. N. Vo and J. Hays · 2016
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G. Lin, A. Milan, C. Shen, and I. Reid · 2017
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Dual discriminator generative adversarial nets
T. D. Nguyen, T. Le, H. Vu, and D. Phung · 2017
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Transformation-grounded image generation network for novel 3d view synthesis
E. Park, J. Yang, E. Yumer, D. Ceylan, and A. C. Berg · 2017
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Predicting ground-level scene layout from aerial imagery
M. Zhai, Z. Bessinger, S. Workman, and N. Jacobs · 2017
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. Metaxas · 2017
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Places: A 10 million image database for scene recognition
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networkss
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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Pros and cons of gan evaluation measures
A. Borji · 2018
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