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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Vydané v:2013 IEEE Conference on Computer Vision and Pattern Recognition s. 391 - 398
Hlavní autori: Bristow, Hilton, Eriksson, Anders, Lucey, Simon
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2013
Predmet:
ADMM
Convergence
Convolution
Convolutional codes
deep learning
Encoding
Equations
fourier
Signal processing algorithms
sparse coding
Vectors
ISSN:1063-6919, 1063-6919
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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
  surname: Eriksson
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  email: anders.eriksson@adelaide.edu.au
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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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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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