What regularized auto-encoders learn from the datagenerating distribution, The Journal of Machine Learning Research, vol.15, issue.1, pp.3563-3593, 2014. ,
End-to-end optimized image compression, 2016. ,
Non-local manifold tangent learning, Advances in Neural Information Processing Systems, 2005. ,
URL : https://hal.archives-ouvertes.fr/hal-01575349
Auto-association by multilayer perceptrons and singular value decomposition, Biological Cybernetics, vol.13, issue.4-5, pp.291-294, 1988. ,
DOI : 10.1109/MASSP.1987.1165576
Generating images with perceptual similarity metrics based on deep networks, Advances in Neural Information Processing Systems, pp.658-666, 2016. ,
Deep sparse rectifier neural networks, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00752497
Deep learning, 2016. ,
A neural representation of sketch drawings. arXiv preprint, 2017. ,
Dimensionality Reduction by Learning an Invariant Mapping, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), 2006. ,
DOI : 10.1109/CVPR.2006.100
Auto-encoding variational bayes, International Conference on Learning Representations, 2014. ,
Learning processes in an asymmetric threshold network, 1987. ,
Graph Regularized Auto-Encoders for Image Representation, IEEE Transactions on Image Processing, vol.26, issue.6, pp.2839-2852, 2017. ,
DOI : 10.1109/TIP.2016.2605010
K-sparse autoencoders. arXiv preprint, 2013. ,
Unsupervised representation learning with deep convolutional generative adversarial networks, 2015. ,
Sparse feature learning for deep belief networks, Conference on Neural Information Processing Systems, 2007. ,
Generative adversarial text to image synthesis. arXiv preprint, 2016. ,
Contractive autoencoders: Explicit invariance during feature extraction, Proceedings of the 28th international conference on machine learning, 2011. ,
Improved techniques for training gans, Advances in Neural Information Processing Systems, pp.2234-2242, 2016. ,
Generative Visual Manipulation on the Natural Image Manifold, European Conference on Computer Vision, 2016. ,
DOI : 10.1109/CVPR.2013.299
URL : http://arxiv.org/pdf/1609.03552