CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.08 - Aug.

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Issue No.08 - Aug. (2013 vol.35)

pp: 1887-1901

Bo Chen , Duke University, Durham

Gungor Polatkan , Princeton University, Princeton

Guillermo Sapiro , Duke University, Durham

David Blei , Princeton University, Princeton

David Dunson , Duke University, Durham

Lawrence Carin , Duke University, Durham

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.19

ABSTRACT

Unsupervised multilayered (“deep”) models are considered for imagery. The model is represented using a hierarchical convolutional factor-analysis construction, with sparse factor loadings and scores. The computation of layer-dependent model parameters is implemented within a Bayesian setting, employing a Gibbs sampler and variational Bayesian (VB) analysis that explicitly exploit the convolutional nature of the expansion. To address large-scale and streaming data, an online version of VB is also developed. The number of dictionary elements at each layer is inferred from the data, based on a beta-Bernoulli implementation of the Indian buffet process. Example results are presented for several image-processing applications, with comparisons to related models in the literature.

INDEX TERMS

Dictionaries, Convolution, Computational modeling, Mathematical model, Analytical models, Load modeling, Bayesian methods, factor analysis, Bayesian, deep learning, convolutional, dictionary learning

CITATION

Bo Chen, Gungor Polatkan, Guillermo Sapiro, David Blei, David Dunson, Lawrence Carin, "Deep Learning with Hierarchical Convolutional Factor Analysis",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.35, no. 8, pp. 1887-1901, Aug. 2013, doi:10.1109/TPAMI.2013.19REFERENCES