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Theories for Deep Learning

Deep CNN Sparse Coding Analysis


Deep Convolutional Sparse Coding (D-CSC) is a framework reminiscent
of deep convolutional neural nets (DCNN), but by omitting the learning of the
dictionaries one can more transparently analyse the role of the
activation function and its ability to recover activation paths
through the layers. Papyan, Romano, and Elad conducted an analysis of
such an architecture \cite{2016arXiv160708194P}, demonstrated the
relationship with DCNNs and proved conditions under which a D-CSC is
guaranteed to recover activation paths. A technical innovation of

Paper Details

Authors:
Michael Murray, Jared Tanner
Submitted On:
31 May 2018 - 12:05pm
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Deep CNN Sparse Coding Analysis.pdf

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[1] Michael Murray, Jared Tanner, "Deep CNN Sparse Coding Analysis", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3225. Accessed: Aug. 18, 2018.
@article{3225-18,
url = {http://sigport.org/3225},
author = {Michael Murray; Jared Tanner },
publisher = {IEEE SigPort},
title = {Deep CNN Sparse Coding Analysis},
year = {2018} }
TY - EJOUR
T1 - Deep CNN Sparse Coding Analysis
AU - Michael Murray; Jared Tanner
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3225
ER -
Michael Murray, Jared Tanner. (2018). Deep CNN Sparse Coding Analysis. IEEE SigPort. http://sigport.org/3225
Michael Murray, Jared Tanner, 2018. Deep CNN Sparse Coding Analysis. Available at: http://sigport.org/3225.
Michael Murray, Jared Tanner. (2018). "Deep CNN Sparse Coding Analysis." Web.
1. Michael Murray, Jared Tanner. Deep CNN Sparse Coding Analysis [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3225