Fetching the paper…
Reading the bibliography…
Deep image translation methods have recently shown excellent results, outputting high-quality images covering multiple modes of the data distribution.
Recovering intrinsic scene characteristics
H. Barrow and J. Tenenbaum · 1978
Earlier work this paper cites.
Separating style and content
J. B. Tenenbaum and W. T. Freeman · 1997
Earlier work this paper cites.
The mnist database of handwritten digits
Y. LeCun · 1998
Earlier work this paper cites.
Recovering intrinsic images from a single image
M. F. Tappen, W. T. Freeman, and E. H. Adelson · 2003
Earlier work this paper cites.
Transforming auto-encoders
G. E. Hinton, A. Krizhevsky, and S. D. Wang · 2011
Earlier work this paper cites.
3d object detection and viewpoint estimation with a deformable 3d cuboid model
S. Fidler, S. Dickinson, and R. Urtasun · 2012
Earlier work this paper cites.
Disentangling factors of variation for facial expression recognition
S. Rifai, Y. Bengio, A. Courville, P. Vincent, and M. Mirza · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
Earlier work this paper cites.
Spatial pattern templates for recognition of objects with regular structure
R. Tyleček and R. Šára · 2013
Earlier work this paper cites.
Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models
M. Aubry, D. Maturana, A. A. Efros, B. C. Russell, and J. Sivic · 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.
Semi-supervised learning with deep generative models
D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling · 2014
Earlier work this paper cites.
Learning to disentangle factors of variation with manifold interaction
S. Reed, K. Sohn, Y. Zhang, and H. Lee · 2014
Earlier work this paper cites.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
Cited alongside, same era.
Deep visual analogy-making
S. E. Reed, Y. Zhang, Y. Zhang, and H. Lee · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Cited alongside, same era.
Domain separation networks
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
Cited alongside, same era.
Adversarial feature learning
J. Donahue, P. Krähenbühl, and T. Darrell · 2017
Later among the works it cites.
Adversarially learned inference
V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville · 2017
Later among the works it cites.
Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
Later among the works it cites.
Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
Later among the works it cites.
Cross-domain image retrieval with attention modeling
X. Ji, W. Wang, M. Zhang, and Y. Yang · 2017
Later among the works it cites.
Cross-domain generative learning for fine-grained sketch-based image retrieval
K. Pang, Y.-Z. Song, T. Xiang, and T. Hospedales · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification
S. Iizuka, E. Simo-Serra, and H. Ishikawa · 2016
Cited alongside, same era.
Precomputed real-time texture synthesis with markovian generative adversarial networks
C. Li and M. Wand · 2016
Cited alongside, same era.
Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Cited alongside, same era.
Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Cited alongside, same era.
Disentangled representation learning gan for pose-invariant face recognition
L. Tran, X. Yin, and X. Liu · 2017
Later among the works it cites.
Augmented cyclegan: Learning many-to-many mappings from unpaired data
A. Almahairi, S. Rajeswar, A. Sordoni, P. Bachman, and A. Courville · 2018
Closest in time.
Learning anonymized representations with adversarial neural networks
C. Feutry, P. Piantanida, Y. Bengio, and P. Duhamel · 2018
Closest in time.
Multimodal unsupervised image-to-image translation
X. Huang, M.-Y. Liu, S. Belongie, and J. Kautz · 2018
Closest in time.
Diverse image-to-image translation via disentangled representations
H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. Singh, and M.-H. Yang · 2018
Closest in time.
Detach and adapt: Learning cross-domain disentangled deep representation
Y.-C. Liu, Y.-Y. Yeh, T.-C. Fu, S.-D. Wang, W.-C. Chiu, and Y.-C. F. Wang · 2018
Closest in time.
Disentangled person image generation
L. Ma, Q. Sun, S. Georgoulis, L. Van Gool, B. Schiele, and M. Fritz · 2018
Closest in time.
Mix and match networks: encoder-decoder alignment for zero-pair image translation
Y. Wang, J. van de Weijer, and L. Herranz · 2018
Closest in time.
The unreasonable effectiveness of deep networks as a perceptual metric
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang · 2018
Closest in time.