Learning frame models using CNN filters

30th AAAI Conference on Artificial Intelligence, AAAI 2016

Volumn , Issue , 2016, Pages 1902-1910

  Lu, Yang ^a   Zhu, Song Chun ^a   Wu, Ying Nian ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BANDPASS FILTERS; COMPUTER VISION; CONVOLUTION; NEURAL NETWORKS;

CNN FILTERS; CONVOLUTIONAL NEURAL NETWORK; FRAME MODELS; LATENT VARIABLE; NATURAL SCENES; PRODUCT OF EXPERTS; RANDOM FIELDS; TEXTURE PATTERNS;

LEARNING ALGORITHMS;

EID: 85007240797     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. Dai, J.; Lu, Y.; and Wu, Y. N. 2015. Generative modeling of convolutional neural networks. In ICLR.
2. Della Pietra, S.; Della Pietra, V.; and Lafferty, J. 1997. Inducing features of random fields. PAMI 19(4):380-393.
3. Dempster, A. P.; Laird, N. M.; and Rubin, D. B. 1977. Maximum likelihood from incomplete data via the em algorithm. Journal of the royal statistical society. Series B (methodological) 1-38.
4. Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei- Fei, L. 2009. Imagenet: A large-scale hierarchical image database. In CVPR, 248-255. IEEE.
5. Denton, E.; Chintala, S.; Szlam, A.; and Fergus, R. 2015. Deep generative image models using a laplacian pyramid of adversarial networks. ArXiv e-prints.
6. Dosovitskiy, E.; Springenberg, J. T.; and Brox, T. 2015. Learning to generate chairs with convolutional neural networks. In CVPR.
7. Gatys, L. A.; Ecker, A. S.; and Bethge, M. 2015. A neural algorithm of artistic style. ArXiv e-prints.
8. Gregor, K.; Danihelka, I.; Graves, A.; Rezende, D. J.; and Wierstra, D. 2015. DRAW: A recurrent neural network for image generation. In ICML, 1462-1471.
9. Hinton, G.; Dayan, P.; Frey, B. J.; and Neal, R. M. 1995. The wake-sleep algorithm for unsupervised neural networks.
10. Hinton, G.; Osindero, S.;Welling, M.; and Teh, Y.-W. 2006. Unsupervised discovery of nonlinear structure using contrastive backpropagation. Cognitive science 30(4):725-731.
11. Hinton, G. E. 2002. Training products of experts by minimizing contrastive divergence. Neural Computation 14(8):1771-1800.
12. Hong, Y.; Si, Z.; Hu, W.; Zhu, S.; and Wu, Y. 2014. Unsupervised learning of compositional sparse code for natural image representation. Quarterly of Applied Mathematics 79:373-406.
13. Julesz, B. 1962. Visual pattern discrimination. IRE Transactions on Information Theory 8(2):84-92.
14. Kingma, D. P., and Welling, M. 2014. Auto-encoding variational bayes. ICLR.
15. Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. In NIPS, 1097-1105.
16. Kulkarni, T. D.; Whitney, W.; Kohli, P.; and Tenenbaum, J. B. 2015. Deep Convolutional Inverse Graphics Network. ArXiv e-prints.
17. LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11):2278-2324.
18. Mnih, A., and Gregor, K. 2014. Neural variational inference and learning in belief networks. In ICML.
19. Ngiam, J.; Chen, Z.; Koh, P.W.; and Ng, A. Y. 2011. Learning deep energy models. In ICML.
20. Rezende, D. J.; Mohamed, S.; and Wierstra, D. 2014. Stochastic backpropagation and approximate inference in deep generative models. In Jebara, T., and Xing, E. P., eds., ICML, 1278-1286. JMLR Workshop and Conference Proceedings.
21. Simonyan, K., and Zisserman, A. 2015. Very deep convolutional networks for large-scale image recognition. ICLR.
22. Simonyan, K.; Vedaldi, A.; and Zisserman, A. 2015. Deep inside convolutional networks: Visualising image classification models and saliency maps. ICLR.
23. Vedaldi, A., and Lenc, K. 2014. Matconvnet - convolutional neural networks for matlab. CoRR abs/1412.4564.
24. Wu, Y. N.; Si, Z.; Gong, H.; and Zhu, S.-C. 2010. Learning active basis model for object detection and recognitio. IJCV 90:198-235.
25. Wu, Y. N.; Zhu, S.-C.; and Liu, X. 2000. Equivalence of julesz ensembles and frame models. IJCV 38:247-265.
26. Xie, J.; Hu, W.; Zhu, S.-C.; and Wu, Y. N. 2015a. Learning sparse frame models for natural image patterns. IJCV 114:91-112.
27. Xie, J.; Lu, Y.; Zhu, S.-C.; and Wu, Y. N. 2015b. Inducing wavelets into random fields via generative boosting. Journal of Applied and Computational Harmonic Analysis.
28. Younes, L. 1999. On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates. Stochastics: An International Journal of Probability and Stochastic Processes 65(3-4):177-228.
29. Zeiler, M. D., and Fergus, R. 2014. Visualizing and understanding convolutional neural networks. ECCV.
30. Zhu, S., and Mumford, D. 1998. Grade: Gibbs reaction and diffusion equations. In ICCV.
31. Zhu, S. C., and Mumford, D. 2006. A stochastic grammar of images. Foundations and Trends in Computer Graphics and Vision 2(4):259-362.
32. Zhu, S. C.; Liu, X.; and Wu, Y. N. 2000. Exploring texture ensembles by efficient markov chain monte carlo - Towards a'trichromacy' theory of texture. PAMI 22:245-261.
33. Zhu, S. C.;Wu, Y. N.; and Mumford, D. 1997. Minimax entropy principle and its application to texture modeling. Neural Computation 9(8):1627-1660.