Learning a Generative Model of Images by Factoring Appearance and Shape
暂无分享,去创建一个
[1] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[2] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[3] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .
[4] David Haussler,et al. Unsupervised learning of distributions on binary vectors using two layer networks , 1991, NIPS 1991.
[5] Charles A. Bouman,et al. A multiscale random field model for Bayesian image segmentation , 1994, IEEE Trans. Image Process..
[6] Alan S. Willsky,et al. Likelihood calculation for a class of multiscale stochastic models, with application to texture discrimination , 1995, IEEE Trans. Image Process..
[7] Elie Bienenstock,et al. Compositionality, MDL Priors, and Object Recognition , 1996, NIPS.
[8] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[9] Christopher K. I. Williams,et al. DTs: Dynamic Trees , 1998, NIPS.
[10] Yee Whye Teh,et al. Learning to Parse Images , 1999, NIPS.
[11] Bruno A. Olshausen,et al. PROBABILISTIC FRAMEWORK FOR THE ADAPTATION AND COMPARISON OF IMAGE CODES , 1999 .
[12] Aapo Hyvärinen,et al. Topographic Independent Component Analysis , 2001, Neural Computation.
[13] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[14] Brendan J. Frey,et al. Learning appearance and transparency manifolds of occluded objects in layers , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[15] Christopher K. I. Williams,et al. Image Modeling with Position-Encoding Dynamic Trees , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[16] Geoffrey E. Hinton,et al. Exponential Family Harmoniums with an Application to Information Retrieval , 2004, NIPS.
[17] David Mumford,et al. Occlusion Models for Natural Images: A Statistical Study of a Scale-Invariant Dead Leaves Model , 2004, International Journal of Computer Vision.
[18] Song-Chun Zhu,et al. Modeling Visual Patterns by Integrating Descriptive and Generative Methods , 2004, International Journal of Computer Vision.
[19] Christopher K. I. Williams,et al. Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning , 2004, Neural Computation.
[20] Michael J. Black,et al. Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[21] Guillaume Bouchard,et al. Hierarchical part-based visual object categorization , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[22] Zhuowen Tu,et al. Image Parsing: Unifying Segmentation, Detection, and Recognition , 2005, International Journal of Computer Vision.
[23] Nebojsa Jojic,et al. LOCUS: learning object classes with unsupervised segmentation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[24] Brendan J. Frey,et al. Generative Model for Layers of Appearance and Deformation , 2005, AISTATS.
[25] Philipp Slusallek,et al. Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.
[26] Stuart Geman,et al. Context and Hierarchy in a Probabilistic Image Model , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[27] Carsten Rother,et al. Clustering appearance and shape by learning jigsaws , 2006, NIPS.
[28] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[29] Sanja Fidler,et al. Towards Scalable Representations of Object Categories: Learning a Hierarchy of Parts , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[30] Honglak Lee,et al. Sparse deep belief net model for visual area V2 , 2007, NIPS.
[31] Song-Chun Zhu,et al. Primal sketch: Integrating structure and texture , 2007, Comput. Vis. Image Underst..
[32] Geoffrey E. Hinton,et al. Modeling image patches with a directed hierarchy of Markov random fields , 2007, NIPS.
[33] Yoshua Bengio,et al. An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.
[34] Ruslan Salakhutdinov,et al. On the quantitative analysis of deep belief networks , 2008, ICML '08.
[35] Narendra Ahuja,et al. Unsupervised Category Modeling, Recognition, and Segmentation in Images , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] Tijmen Tieleman,et al. Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.
[37] Long Zhu,et al. Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion , 2008, ECCV.
[38] Honglak Lee,et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.
[39] Xinqi. Chu. Modeling of visual patterns. , 2009 .
[40] Ruslan Salakhutdinov,et al. Learning in Markov Random Fields using Tempered Transitions , 2009, NIPS.
[41] Rajat Raina,et al. Large-scale deep unsupervised learning using graphics processors , 2009, ICML '09.
[42] Michael S. Lewicki,et al. Emergence of complex cell properties by learning to generalize in natural scenes , 2009, Nature.
[43] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[44] Joachim M. Buhmann,et al. Learning the Compositional Nature of Visual Object Categories for Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.