H. Amir-arsalan-soltani, J. Huang, . Wu, D. Tejas, J. B. Kulkarni et al., Synthesizing 3d shapes via modeling multi-view depth maps and silhouettes with deep generative networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1511-1519, 2017.

M. Aubry, D. Maturana, A. Efros, B. Russell, and J. Sivic, Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models, CVPR, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01057240

X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever et al., Infogan: Interpretable representation learning by information maximizing generative adversarial nets, Advances in Neural Information Processing Systems, pp.2172-2180, 2016.

A. C. Damianou, C. H. Ek, M. K. Titsias, and N. D. Lawrence, Manifold relevance determination, Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2012.

E. Denton and V. Birodkar, Unsupervised learning of disentangled representations from video, 2017.

H. Edwards and A. Storkey, Censoring representations with an adversary, 2015.

I. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., Generative adversarial nets, Advances in neural information processing systems, pp.2672-2680, 2014.

I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot et al., Shakir Mohamed, and Alexander Lerchner. beta-vae: Learning basic visual concepts with a constrained variational framework, 2016.

P. Isola, J. Zhu, T. Zhou, and A. A. Efros, Image-to-image translation with conditional adversarial networks, 2016.

T. Kim, M. Cha, H. Kim, J. Lee, and J. Kim, Learning to discover cross-domain relations with generative adversarial networks, 2017.

T. Kim, B. Kim, M. Cha, and J. Kim, Unsupervised visual attribute transfer with reconfigurable generative adversarial networks, 2017.

P. Diederik, J. Kingma, and . Ba, Adam: A method for stochastic optimization. CoRR, abs/1412, vol.6980, 2014.

A. Klami, S. Virtanen, and S. Kaski, Bayesian canonical correlation analysis, Journal of Machine Learning Research, vol.14, issue.1, pp.965-1003, 2013.

G. Lample, N. Zeghidour, N. Usunier, A. Bordes, L. Denoyer et al., Fader networks: Manipulating images by sliding attributes, 2017.

A. Li, S. Shan, X. Chen, B. Ma, S. Yan et al., Cross-pose face recognition by canonical correlation analysis, 2015.

. Kuan-hsien, T. Liu, C. Chen, and . Chen, Mvc: A dataset for view-invariant clothing retrieval and attribute prediction, Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, pp.313-316, 2016.

M. Liu, T. Breuel, and J. Kautz, Unsupervised image-to-image translation networks, 2017.

Z. Liu, P. Luo, X. Wang, and X. Tang, Deep learning face attributes in the wild, Proceedings of International Conference on Computer Vision (ICCV), 2015.

C. Louizos, K. Swersky, Y. Li, M. Welling, and R. Zemel, The variational fair autoencoder, 2015.

F. Michael, J. J. Mathieu, J. Zhao, A. Zhao, P. Ramesh et al., Disentangling factors of variation in deep representation using adversarial training, Advances in Neural Information Processing Systems, pp.5040-5048, 2016.

M. Mirza and S. Osindero, Conditional generative adversarial nets, 2014.

M. Nilsback and A. Zisserman, Automated flower classification over a large number of classes, Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, 2008.

M. Omkar, A. Parkhi, A. Vedaldi, and . Zisserman,

H. Charles-r-qi, M. Su, A. Nießner, M. Dai, L. J. Yan et al., Volumetric and multi-view cnns for object classification on 3d data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5648-5656, 2016.

A. Radford, L. Metz, and S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, 2015.

H. Su and S. Maji, Evangelos Kalogerakis, and Erik Learned-Miller. Multi-view convolutional neural networks for 3d shape recognition, Proceedings of the IEEE international conference on computer vision, pp.945-953, 2015.

Q. Tang, W. Wang, and K. Livescu, Acoustic feature learning via deep variational canonical correlation analysis. CoRR, abs/1708.04673, 2017.

R. Ledyard and . Tucker, An inter-battery method of factor analysis, Psychometrika, vol.23, issue.2, pp.111-136, 1958.

B. Zhao, X. Wu, Z. Cheng, H. Liu, and J. Feng, Multi-view image generation from a single-view, 2017.

J. Zhu, T. Park, P. Isola, and A. A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, 2017.