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|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.|
|Citation:||Chen, Bo, Gungor Polatkan, Guillermo Sapiro, David Blei, David Dunson, and Lawrence Carin. "Deep learning with hierarchical convolutional factor analysis." IEEE transactions on pattern analysis and machine intelligence 35, no. 8 (2013): 1887-1901. doi:10.1109/TPAMI.2013.19|
|Pages:||1887 - 1901|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||IEEE Transactions on Pattern Analysis and Machine Intelligence|
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