Image Compression Using Deep Auto Encoder

1. Gedeon, T. D., Harris, D.: Progressive image compression. In Neural Networks, 1992. IJCNN., International Joint Conference on (Vol.
4, pp. 403-407). IEEE (1992).
2. Gedeon, T. D., Harris, D.:Network reduction techniques. In Proceedings International Conference on Neural Networks Methodologies
and Applications, Vol. 1, pp. 119-126 (1991)
3. Jolli_e, I. T.: Principal component analysis and factor analysis. In Principal component analysis pp. 115- 128. Springer, New York,
NY(1986)..
4. Nasrabadi, N. M. : Pattern recognition and machine learning. Journal of electronic imaging, 16(4), 049901 (2007).
5. Dua, D., Karra Taniskidou, E.: UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine,CA: University of California,
School of Information and Computer Science(1986).
6. Sra, S., Dhillon, I. S.:Generalized nonnegative matrix approximations with Bregman divergences. In Ad-vances in neural information
processing systems pp. 283-290 (2006).
7. Kingma, D. P., Welling, M.:Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013).
8. Kingma, Diederik P., and Jimmy Ba. "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980 (2014).
9. Mao, X., Shen, C., Yang, Y. B.:Image restoration using very deep convolutional encoder-decoder networkswith symmetric skip
connections. In Advances in neural information processing systems pp. 2802-2810(2016).
9. Mao, X., Shen, C., Yang, Y. B.:Image restoration using very deep convolutional encoder-decoder networkswith symmetric skip
connections. In Advances in neural information processing systems pp. 2802-2810(2016).
10. Huang, G. B., Ramesh, M., Berg, T., & Learned-Miller, E. ( Labeled faces in the wild: A database forstudying face recognition in
unconstrained environments (Vol. 1, No. 2, p. 3). Technical Report 07-49,University of Massachusetts, Amherst (2007).
11. Maaten, L. V. D., & Hinton, G. Visualizing data using t-SNE. Journal of machine learning research,9(Nov), 2579-2605(2008).
12. Baldi, P. Autoencoders, unsupervised learning, and deep architectures. In Proceedings of ICML Workshop on Unsupervised and
Transfer Learning (pp. 37-49)(2012, June).
13. Krizhevsky, A., Sutskever, I., & Hinton, G. E. Imagenet classi_cation with deep convolutional neuralnetworks. In Advances in neural
information processing systems (pp. 1097-1105)(2012).
14. LeCun, Y. (2015). LeNet-5, convolutional neural networks. URL: http://yann. lecun. com/exdb/lenet, 20.