Fast Convolutional Sparse Coding

Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks. The canonical approach intrinsically assumes independence between observations during learning. For many natural signals however, sparse coding is applied...

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Published in:2013 IEEE Conference on Computer Vision and Pattern Recognition pp. 391 - 398
Main Authors: Bristow, Hilton, Eriksson, Anders, Lucey, Simon
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2013
Subjects:
ADMM
Convergence
Convolution
Convolutional codes
deep learning
Encoding
Equations
fourier
Signal processing algorithms
sparse coding
Vectors
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Abstract Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks. The canonical approach intrinsically assumes independence between observations during learning. For many natural signals however, sparse coding is applied to sub-elements ( i.e. patches) of the signal, where such an assumption is invalid. Convolutional sparse coding explicitly models local interactions through the convolution operator, however the resulting optimization problem is considerably more complex than traditional sparse coding. In this paper, we draw upon ideas from signal processing and Augmented Lagrange Methods (ALMs) to produce a fast algorithm with globally optimal sub problems and super-linear convergence.
AbstractList Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks. The canonical approach intrinsically assumes independence between observations during learning. For many natural signals however, sparse coding is applied to sub-elements ( i.e. patches) of the signal, where such an assumption is invalid. Convolutional sparse coding explicitly models local interactions through the convolution operator, however the resulting optimization problem is considerably more complex than traditional sparse coding. In this paper, we draw upon ideas from signal processing and Augmented Lagrange Methods (ALMs) to produce a fast algorithm with globally optimal sub problems and super-linear convergence.
Author Bristow, Hilton
Lucey, Simon
Eriksson, Anders
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  givenname: Anders
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  givenname: Simon
  surname: Lucey
  fullname: Lucey, Simon
  email: simon.lucey@csiro.au
  organization: CSIRO, Sydney, NSW, Australia
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Snippet Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks. The canonical...
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StartPage 391
SubjectTerms ADMM
Convergence
Convolution
Convolutional codes
deep learning
Encoding
Equations
fourier
Signal processing algorithms
sparse coding
Vectors
Title Fast Convolutional Sparse Coding
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