Autoencoding beyond pixels using a learned similarity metric

33rd International Conference on Machine Learning, ICML 2016

Volumn 4, Issue , 2016, Pages 2341-2349

  Larsen, Anders Boesen Lindbo ^a   Sønderby, Søren Kaae ^b   Larochelle, Hugo ^c   Winther, Ole ^a,b  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

^c TWITTER INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

ADVERSARIAL NETWORKS; AUTO ENCODERS; DATA DISTRIBUTION; FEATURE REPRESENTATION; SIMILARITY MEASURE; SIMILARITY METRICS; VISUAL FEATURE; VISUAL FIDELITY;

LEARNING SYSTEMS;

EID: 84999041243     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Bengio, Yoshua, Courville, Aaron, and Vincent, Pierre. Representation learning: A review and new perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(8): 1798-1828, 2013.
2. Bromley, Jane, Bentz, James W., Bottou, Leon, Guyon, Isabelle, LeCun, Yann, Moore, Cliff, Sackinger, Ed- uard, and Shah, Roopak. Signature verification using a siamese time delay neural network. International Journal of Pattern Recognition and Artificial Intelligence, 07 (04):669-688, 1993.
3. Chopra, S., Hadsell, R., and LeCun, Y. Learning a similarity metric discriminatively, with application to face verification. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pp. 539-546 vol. 1, June 2005.
4. Denton, Emily L, Chintala, Soumith, Szlam, Arthur, and Fergus, Rob. Deep generative image models using a laplacian pyramid of adversarial networks. In Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R., and Garnett, R. (eds.), Advances in Neural Information Processing Systems 28, pp. 1486-1494. Curran Associates, Inc., 2015.
5. Dosovitskiy, Alexey, Springenberg, Jost Tobias, and Brox, Thomas. Learning to generate chairs with convolutional neural networks. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1538-1546, 2015.
6. Gatys, Leon A., Ecker, Alexander S., and Bethge, Matthias. A neural algorithm of artistic style. CoRR, abs/1508.06576, 2015.
7. Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua. Generative adversarial nets. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., and Weinberger, K.Q. (eds.), Advances in Neural Information Processing Systems 27, pp. 2672-2680. Curran Associates, Inc., 2014.
8. Huang, Gary B., Ramesh, Manu, Berg, Tamara, and Learned-Miller, Erik. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst, October 2007.
9. Ioffe, Sergey and Szegedy, Christian. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Blei, David and Bach, Francis (eds.), Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pp. 448-456. JMLR Workshop and Conference Proceedings, 2015.
10. Kingma, Diederik P. and Welling, Max. Auto-encoding variational Bayes. In Proceedings of the International Conference on Learning Representations, 2014.
11. Kulkarni, Tejas D., Whitney, Will, Kohli, Pushmeet, and Tenenbaum, Joshua B. Deep convolutional inverse graphics network. CoRR, abs/1503.03167, 2015.
12. Kumar, Neeraj, Berg, Alexander C., Belhumeur, Peter N, and Nayar, Shree K. Attribute and simile classifiers for face verification. In Computer Vision, 2009 IEEE 12th International Conference on, pp. 365-372, Sept 2009.
13. Larsen, Anders, Boesen Lindbo. CUDArray: CUDA-based NumPy. Technical Report DTU Compute 2014-21, Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2014.
14. Liu, Ziwei, Luo, Ping, Wang, Xiaogang, and Tang, Xiaoou. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), 2015.
15. Mansimov, Elman, Parisotto, Emilio, Ba, Lei Jimmy, and Salakhutdinov, Ruslan. Generating images from captions with attention. CoRR, abs/1511.02793, 2015.
16. Mathieu, Michael, Couprie, Camille, and LeCun, Yann. Deep multi-scale video prediction beyond mean square error. CoRR, abs/1511.05440, 2015.
17. Mikolov, Tomas, Sutskever, Ilya, Chen, Kai, Corrado, Greg S, and Dean, Jeff. Distributed representations of words and phrases and their compositionality. In Burges, C.J.C., Bottou, L, Welling, M, Ghahramani, Z, and Weinberger, K.Q. (eds.), Advances in Neural Information Processing Systems 26, pp. 3111-3119. Curran Associates, Inc., 2013.
18. Mirza, Mehdi and Osindero, Simon. Conditional generative adversarial nets. CoRR, abs/1411.1784, 2014.
19. Radford, Alec, Metz, Luke, and Chintala, Soumith. Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR, abs/1511.06434, 2015.
20. Rasmus, Antti, Berglund, Mathias, Honkala, Mikko, Valpola, Harri, and Raiko, Tapani. Semi-supervised learning with ladder networks. In Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., and Garnett, R. (eds.), Advances in Neural Information Processing Systems 28, pp. 3532-3540. Curran Associates, Inc., 2015.
21. Reed, Scott E, Zhang, Yi, Zhang, Yuting, and Lee, Honglak. Deep visual analogy-making. In Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R., and Garnett, R. (eds.), Advances in Neural Information Processing Systems 28, pp. 1252-1260. Curran Associates, Inc., 2015.
22. Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan. Stochastic backpropagation and approximate inference in deep generative models. In Proceedings of The 31st International Conference on Machine Learning, pp. 1278-1286, 2014.
23. Ridgeway, Karl, Snell, Jake, Roads, Brett, Zemel, Richard S., and Mozer, Michael C. Learning to generate images with perceptual similarity metrics. CoRR, abs/1511.06409, 2015.
24. Theis, Lucas, van den Oord, Aaron, and Bethge, Matthias. A note on the evaluation of generative models. CoRR, abs/1511.01844, 2015.
25. Wang, Zhou and Bovik, A.C. Mean squared error: Love it or leave it? a new look at signal fidelity measures. Signal Processing Magazine, IEEE, 26(1):98-117, Jan 2009.
26. Yan, X., Yang, J., Sohn, K., and Lee, H. Attribute2Image: Conditional Image Generation from Visual Attributes. CoRR, abs/1512.00570, 2015.
27. Zhao, Junbo, Mathieu, Michael, Goroshin, Ross, and LeCun, Yann. Stacked what-where auto-encoders. CoRR, abs/1506.02351, 2015.
28. Zhu, Shizhan, Li, Cheng, Loy, Chen Change, and Tang, Xiaoou. Transferring landmark annotations for cross- dataset face alignment. CoRR, abs/1409.0602, 2014.