title: Deep learning with hierarchical convolutional factor analysis.
abstract: Unsupervised multilayered (&#x201C;deep&#x201D;) 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.
authors.affiliation: Electrical and Computer Engineering Department, Duke University, 130 Hudson Hall, Durham, NC 27708-0291, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Chen
authors.forename: Gungor
authors.initials: G
authors.lastname: Polatkan
authors.forename: Guillermo
authors.initials: G
authors.lastname: Sapiro
authors.forename: David
authors.initials: D
authors.lastname: Blei
authors.forename: David
authors.initials: D
authors.lastname: Dunson
authors.forename: Lawrence
authors.initials: L
authors.lastname: Carin