Z. Xia, X. Feng, J. Peng, and A. Hadid, Unsupervised deep hashing for large-scale visual search, 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), 2016.
DOI : 10.1109/IPTA.2016.7821007
URL : http://arxiv.org/abs/1602.00206

J. Zhao, M. Mathieu, R. Goroshin, and Y. Lecun, Stacked what-where auto-encoders, 2015.

J. Wang, S. Kumar, and S. Chang, Semisupervised hashing for scalable image retrieval, IEEE Conference on Computer Vision and Pattern Recognition, pp.3424-3431, 2010.
DOI : 10.1109/cvpr.2010.5539994
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.4032

K. Lin, H. Yang, J. Hsiao, and C. Chen, Deep learning of binary hash codes for fast image retrieval, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.27-35, 2015.
DOI : 10.1109/CVPRW.2015.7301269

J. Masci, U. Meier, D. Cires¸ancires¸an, and J. Schmidhuber, Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction, International Conference on Artificial Neural Networks, pp.52-59, 2011.
DOI : 10.1162/neco.1996.8.4.773
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.300.2716

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of Machine Learning Research, vol.11

P. Diederik, M. Kingma, and . Welling, Auto-encoding variational bayes, 2013.

J. Wang, T. Zhang, J. Song, N. Sebe, and H. Shen, A Survey on Learning to Hash, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016.
DOI : 10.1109/TPAMI.2017.2699960

J. Wang, W. Liu, S. Kumar, and S. Chang, Learning to Hash for Indexing Big Data—A Survey, Proceedings of the IEEE, vol.104, issue.1, pp.34-57
DOI : 10.1109/JPROC.2015.2487976

W. Liu, J. Wang, R. Ji, Y. Jiang, and S. Chang, Supervised hashing with kernels, IEEE Conference on Computer Vision and Pattern Recognition, pp.2074-2081, 2012.

R. Xia, Y. Pan, H. Lai, C. Liu, and S. Yan, Supervised hashing for image retrieval via image representation learning, AAAI, 2014.

J. Zhang, Y. Peng, and J. Zhang, Ssdh: semi-supervised deep hashing for large scale image retrieval, 2016.

H. Jegou, M. Douze, and C. Schmid, Product Quantization for Nearest Neighbor Search, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.1, pp.117-128, 2011.
DOI : 10.1109/TPAMI.2010.57
URL : https://hal.archives-ouvertes.fr/inria-00514462

Y. Zhang, K. Lee, and H. Lee, Augmenting supervised neural networks with unsupervised objectives for large-scale image classification, 2016.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.9, issue.7553, pp.436-444, 2015.
DOI : 10.1007/s10994-013-5335-x

A. Makhzani, J. Brendan, and . Frey, Winner-takeall autoencoders, Advances in Neural Information Processing Systems, pp.2791-2799, 2015.

A. Gionis, P. Indyk, and R. Motwani, Similarity search in high dimensions via hashing, VLDB, pp.518-529, 1999.

W. Dong, Z. Wang, W. Josephson, M. Charikar, and K. Li, Modeling LSH for performance tuning, Proceeding of the 17th ACM conference on Information and knowledge mining, CIKM '08, pp.669-678, 2008.
DOI : 10.1145/1458082.1458172
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.186.4629

A. Dasgupta, R. Kumar, and T. Sarlós, Fast locality-sensitive hashing, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pp.1073-1081, 2011.
DOI : 10.1145/2020408.2020578

H. Liu, R. Wang, S. Shan, and X. Chen, Deep Supervised Hashing for Fast Image Retrieval, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2064-2072, 2016.
DOI : 10.1109/CVPR.2016.227

Y. Weiss, A. Torralba, and R. Fergus, Spectral hashing, Advances in neural information processing systems, pp.1753-1760, 2009.

J. Wang, S. Kumar, and S. Chang, Semi-Supervised Hashing for Large-Scale Search, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.12, pp.2393-2406, 2012.
DOI : 10.1109/TPAMI.2012.48

Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin, Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.12, pp.2916-2929, 2013.
DOI : 10.1109/TPAMI.2012.193
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.402.3894

N. Dilokthanakul, A. Pedro, M. Mediano, . Garnelo, C. Matthew et al., Deep unsupervised clustering with gaussian mixture variational autoencoders, 2016.