Super-resolution with deep convolutional sufficient statistics

4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings

Volumn , Issue , 2016, Pages

  Bruna, Joan ^a   Sprechmann, Pablo ^b   LeCun, Yann ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; CONVOLUTION; DEEP NEURAL NETWORKS; NEURAL NETWORKS; OPTICAL RESOLVING POWER; TEXTURES;

CONDITIONAL DISTRIBUTION; CONVOLUTIONAL NEURAL NETWORK; HIGH-DIMENSIONAL IMAGES; HIGH-RESOLUTION OUTPUT; IMAGE SUPER RESOLUTIONS; REGRESSION TO THE MEANS; STRUCTURED PREDICTION; SUFFICIENT STATISTICS;

INVERSE PROBLEMS;

EID: 85083954075     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. Bengio, Y., Thibodeau-Laufer, E., Alain, G., and Yosinski, J. Deep generative stochastic networks trainable by backprop. In ICML, 2013.
2. Bruna, J. and Mallat, S. Invariant scattering convolution networks. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(8):1872–1886, 2013.
3. Bruna, J., Szlam, A., and LeCun, Y. Signal recovery from pooling representations. ICML, 2014.
4. Cui, Z., Chang, H., Shan, S., Zhong, B., and Chen, X. Deep network cascade for image super-resolution. In Computer Vision–ECCV, pp. 49–64. Springer, 2014.
5. Dai, Jifeng, Lu, Yang, and Wu, Ying Nian. Generative modeling of convolutional neural networks. In ICLR, 2015.
6. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. Imagenet: A large-scale hierarchical image database. In CVPR, pp. 248–255. IEEE, 2009.
7. Denton, E., Chintala, S., Szlam, A., and Fergus, R. Deep generative image models using a laplacian pyramid of adversarial networks. arXiv preprint arXiv:1506.05751, 2015.
8. Dong, C., Loy, Chen C., He, K., and Tang, X. Learning a deep convolutional network for image super-resolution. In Computer Vision–ECCV, pp. 184–199. Springer, 2014.
9. Elad, M. and Aharon, M. Image denoising via sparse and redundant representations over learned dictionaries. Image Processing, IEEE Transactions on, 15(12):3736–3745, 2006.
10. Freeman, W T, Jones, T R, and Pasztor, E C. Example-based super-resolution. Computer Graphics and Applications, IEEE, 22(2):56–65, 2002.
11. Gatys, Leon A, Ecker, Alexander S, and Bethge, Matthias. Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks. arXiv preprint arXiv:1505.07376, 2015.
12. Goodfellow, I., Le, Q., Saxe, A., Lee, H., and Ng, A. Y. Measuring invariances in deep networks. In NIPS, pp. 646–654. 2009.
13. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. Generative adversarial nets. In NIPS, pp. 2672–2680, 2014.
14. Gregor, K. and LeCun, Y. Learning fast approximations of sparse coding. In ICML, pp. 399–406, 2010.
15. Gregor, K., Danihelka, I., Graves, A., and Wierstra, D. DRAW: A recurrent neural network for image generation. arXiv preprint arXiv:1502.04623, 2015.
16. Hénaff, O. J., Ballé, J., Rabinowitz, N. C., and Simoncelli, E. P. The local low-dimensionality of natural images. arXiv preprint arXiv:1412.6626, 2014.
17. Jimenez-Rezende, D and Mohamed, S. Variational inference with normalizing flows. arXiv preprint arXiv:1505.05770, 2015.
18. Kingma, D. P and Welling, M. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
19. Kingma, Diederik and Ba, Jimmy. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
20. Lamb, Alex, Dumoulin, Vincent, and Courville, Aaron. Discriminative regularization for generative models. arXiv preprint arXiv:1602.03220, 2016.
21. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, pp. 2278–2324, 1998.
22. LeCun, Yann, Chopra, Sumit, Hadsell, Raia, Ranzato, M, and Huang, F. A tutorial on energy-based learning. Predicting structured data, 1:0, 2006.
23. Mairal, J., Bach, F., Ponce, J., and Sapiro, G. Online dictionary learning for sparse coding. In ICML, pp. 689–696, 2009.
24. Mairal, J., Bach, F., and Ponce, J. Task-driven dictionary learning. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(4):791–804, 2012.
25. Mallat, S. Group invariant scattering. Communications on Pure and Applied Mathematics, 65(10): 1331–1398, 2012.
26. Ngiam, Jiquan, Chen, Zhenghao, Koh, Pang W, and Ng, Andrew Y. Learning deep energy models. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 1105–1112, 2011.
27. Oquab, M., Bottou, L., Laptev, I., and Sivic, J. Learning and transferring mid-level image representations using convolutional neural networks. In CVPR, pp. 1717–1724. IEEE, 2014.
28. Portilla, Javier and Simoncelli, Eero P. A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision, 40(1):49–70, 2000.
29. Simonyan, K. and Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
30. Sohl-Dickstein, J, Weiss, E A, Maheswaranathan, N, and Ganguli, S. Deep unsupervised learning using nonequilibrium thermodynamics. arXiv preprint arXiv:1503.03585, 2015.
31. Sprechmann, P., Bronstein, A.M., and Sapiro, G. Learning efficient sparse and low rank models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 37(9):1821–1833, 2015.
32. Xie, J., Lu, Y., Zhu, S., and Wu, Y. A theory of generative convnet. arXiv preprint arXiv:1602:03264, 2016.
33. Yang, J., Wright, J., Huang, T., and Ma, Y. Image super-resolution as sparse representation of raw image patches. In CVPR, pp. 1–8. IEEE, 2008.
34. Yang, J., Wang, Z., Lin, Z., Cohen, S., and Huang, T. S. Coupled dictionary training for image super-resolution. Image Processing, IEEE Transactions on, 21(8):3467–3478, 2012.
35. Zhu, S. C., Wu, Y, and Mumford, D. Filters, random fields and maximum entropy (frame): Towards a unified theory for texture modeling. IJCV, 27(2):107–126, 1998.