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dc.contributor.authorAffara, Lama Ahmed
dc.contributor.authorGhanem, Bernard
dc.contributor.authorWonka, Peter
dc.date.accessioned2017-12-28T07:32:13Z
dc.date.available2017-12-28T07:32:13Z
dc.date.issued2017-09-27
dc.identifier.urihttp://hdl.handle.net/10754/626494
dc.description.abstractConvolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1709.09479v1
dc.relation.urlhttp://arxiv.org/pdf/1709.09479v1
dc.rightsArchived with thanks to arXiv
dc.titleFast Convolutional Sparse Coding in the Dual Domain
dc.typePreprint
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.eprint.versionPre-print
dc.identifier.arxivid1709.09479
kaust.personAffara, Lama Ahmed
kaust.personGhanem, Bernard
kaust.personWonka, Peter
refterms.dateFOA2018-06-14T03:35:54Z


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