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dc.contributor.authorChoudhury, Biswarup
dc.contributor.authorSwanson, Robin J.
dc.contributor.authorHeide, Felix
dc.contributor.authorWetzstein, Gordon
dc.contributor.authorHeidrich, Wolfgang
dc.date.accessioned2017-05-31T11:53:47Z
dc.date.available2017-05-31T11:53:47Z
dc.date.issued2017-04-11
dc.identifier.urihttp://hdl.handle.net/10754/623957
dc.description.abstractConvolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaickingand 4D light field view synthesis.
dc.titleConsensus Convolutional Sparse Coding
dc.typePoster
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentVisual Computing Center (VCC)
dc.conference.dateApril 10-12, 2017
dc.conference.nameKAUST Research Conference 2017: Visual Computing – Modeling and Reconstruction
dc.conference.locationKAUST
dc.contributor.institutionUniversity of Toronto
dc.contributor.institutionStanford University
kaust.personChoudhury, Biswarup
kaust.personHeidrich, Wolfgang
refterms.dateFOA2018-06-13T18:00:47Z


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