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Deep Learning with Hierarchical Convolutional Factor Analysis

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6 Author(s)
Bo Chen ; Electr. & Comput. Eng. Dept., Duke Univ., Durham, NC, USA ; Polatkan, G. ; Sapiro, G. ; Blei, D.
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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.

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:35 ,  Issue: 8 )