Generative image modeling using spatial LSTMs

Advances in Neural Information Processing Systems

Volumn 2015-January, Issue , 2015, Pages 1927-1935

  Theis, Lucas ^a   Bethge, Matthias ^a  

^a UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION SCIENCE;

IMAGE DATASETS; LONG SHORT TERM MEMORY; LONG-RANGE DEPENDENCIES; QUANTITATIVE COMPARISON; SPATIAL STRUCTURE; STATE OF THE ART; STATISTICAL DEPENDENCIES; TEXTURE SYNTHESIS;

RECURRENT NEURAL NETWORKS;

EID: 84965111399     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (50)

1. A. J. Bell and T. J. Sejnowski. The "independent components" of natural scenes are edge filters. Vision Research, 37(23):3327-3338, 1997.
2. P. Brodatz. Textures: A Photographic Album for Artists and Designers. Dover, New York, 1966. URL http://www.ux.uis.no/~tranden/brodatz.html.
3. T. Cover and J. Thomas. Elements of Information Theory. Wiley, 2nd edition, 2006.
4. E. Denton, S. Chintala, A. Szlam, and R. Fergus. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. In Advances in Neural Information Processing Systems 28, 2015.
5. J. Domke, A. Karapurkar, and Y. Aloimonos. Who killed the directed model? In CVPR, 2008.
6. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. DeCAF: A deep convolutional activation feature for generic visual recognition. In ICML 31, 2014.
7. H. E. Gerhard, L. Theis, and M. Bethge. Modeling natural image statistics. In Biologically-inspired Computer Vision-Fundamentals and Applications. Wiley VCH, 2015.
8. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems 27, 2014.
9. A. Graves and J. Schmidhuber. Offline handwriting recognition with multidimensional recurrent neural networks. In Advances in Neural Information Processing Systems 22, 2009.
10. K. Gregor, I. Danihelka, A. Mnih, C. Blundell, and D. Wierstra. Deep AutoRegressive Networks. In Proceedings of the 31st International Conference on Machine Learning, 2014.
11. K. Gregor, I. Danihelka, A. Graves, and D. Wierstra. DRAW: A recurrent neural network for image generation. In Proceedings of the 32nd International Conference on Machine Learning, 2015.
12. N. Heess, C. Williams, and G. E. Hinton. Learning generative texture models with extended fields-ofexperts. In BMCV, 2009.
13. G. Hinton, S. Osindero, and Y. Teh. A fast learning algorithm for deep belief nets. Neural Comp., 2006.
14. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 9(8), 1997.
15. R. Hosseini, F. Sinz, and M. Bethge. Lower bounds on the redundancy of natural images. Vis. Res., 2010.
16. A. Hyvärinen and P. O. Hoyer. Emergence of phase and shift invariant features by decomposition of natural images into independent feature subspaces. Neural Computation, 12(7):1705-1720, 2000.
17. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding, 2014. arXiv:1408.5093.
18. D. P. Kingma and M. Welling. Auto-encoding variational Bayes. In ICLR, 2014.
19. D. P. Kingma, D. J. Rezende, S. Mohamed, and M. Welling. Semi-supervised learning with deep generative models. In Advances in Neural Information Processing Systems 27, 2014.
20. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25, 2012.
21. H. Larochelle and I. Murray. The neural autoregressive distribution estimator. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, 2011.
22. A. B. Lee, D. Mumford, and J. Huang. Occlusion models for natural images: A statistical study of a scale-invariant dead leaves model. International Journal of Computer Vision, 2001.
23. H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In ICML 26, 2009.
24. Y. Li, K. Swersky, and R. Zemel. Generative moment matching networks. In ICML 32, 2015.
25. D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, 2001.
26. G. Matheron. Modele séquential de partition aléatoire. Technical report, CMM, 1968.
27. R. M. Neal. Connectionist learning of belief networks. Artificial Intelligence, 56:71-113, 1992.
28. J. Ngiam, Z. Chen, P. W. Koh, and A. Y. Ng. Learning deep energy models. In ICML 28, 2011.
29. S. Osindero and G. E. Hinton. Modelling image patches with a directed hierarchy of markov random fields. In Advances In Neural Information Processing Systems 20, 2008.
30. M. A. Ranzato, J. Susskind, V. Mnih, and G. E. Hinton. On deep generative models with applications to recognition. In IEEE Conference on Computer Vision and Pattern Recognition, 2011.
31. M. A. Ranzato, A. Szlam, J. Bruna, M. Mathieu, R. Collobert, and S. Chopra. Video (language) modeling: a baseline for generative models of natural videos, 2015. arXiv:1412.6604v2.
32. A. J. Robinson and F. Fallside. The utility driven dynamic error propagation network. Technical report, Cambridge University, 1987.
33. S. Roth and M. J. Black. Fields of experts. International Journal of Computer Vision, 82(2), 2009.
34. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Represenations, 2015.
35. J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In ICML 32, 2015.
36. N. Srivastava and R. Salakhutdinov. Multimodal learning with deep Boltzmann machines. JMLR, 2014.
37. N. Srivastava, E. Mansimov, and R. Salakhutdinov. Unsupervised learning of video representations using LSTMs. In Proceedings of the 32nd International Conference on Machine Learning, 2015.
38. M. Sundermeyer, R. Schluter, and H. Ney. LSTM neural networks for language modeling. In INTER-SPEECH, 2010.
39. I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems 27, 2014.
40. L. Theis, S. Gerwinn, F. Sinz, and M. Bethge. In all likelihood, deep belief is not enough. JMLR, 2011.
41. L. Theis, R. Hosseini, and M. Bethge. Mixtures of conditional Gaussian scale mixtures applied to multiscale image representations. PLoS ONE, 7(7), 2012.
42. L. Theis, J. Sohl-Dickstein, and M. Bethge. Training sparse natural image models with a fast Gibbs sampler of an extended state space. In Advances in Neural Information Processing Systems 25, 2012.
43. L. Tierney. Markov chains for exploring posterior distributions. The Annals of Statistics, 1994.
44. B. Uria, I. Murray, and H. Larochelle. RNADE: the real-valued neural autoregressive density-estimator. In Advances in Neural Information Processing Systems 26, 2013.
45. B. Uria, I. Murray, and H. Larochelle. A deep and tractable density estimator. In ICML 31, 2014.
46. A. van Den Oord and B. Schrauwen. The student-t mixture as a natural image patch prior with application to image compression. Journal of Machine Learning Research, 15(1):2061-2086, 2014.
47. A. van den Oord and B. Schrauwen. Factoring variations in natural images with deep Gaussian mixture models. In Advances in Neural Information Processing Systems 27, 2014.
48. J. H. van Hateren and A. van der Schaaf. Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. of the Royal Society B: Biological Sciences, 265(1394), 1998.
49. D. Zoran and Y. Weiss. From learning models of natural image patches to whole image restoration. In IEEE International Conference on Computer Vision, 2011.
50. D. Zoran and Y. Weiss. Natural images, Gaussian mixtures and dead leaves. In NIPS 25, 2012.