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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Vydáno v:2013 IEEE Conference on Computer Vision and Pattern Recognition s. 391 - 398
Hlavní autoři: Bristow, Hilton, Eriksson, Anders, Lucey, Simon
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2013
Témata:
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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Shrnutí: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.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2013.57