Consensus Convolutional Sparse Coding
dc.contributor.author | Choudhury, Biswarup | |
dc.contributor.author | Swanson, Robin J. | |
dc.contributor.author | Heide, Felix | |
dc.contributor.author | Wetzstein, Gordon | |
dc.contributor.author | Heidrich, Wolfgang | |
dc.date.accessioned | 2018-01-17T13:58:13Z | |
dc.date.available | 2018-01-17T13:58:13Z | |
dc.date.issued | 2017-12-25 | |
dc.identifier.doi | 10.1109/ICCV.2017.459 | |
dc.identifier.uri | http://hdl.handle.net/10754/626836 | |
dc.description.abstract | Convolutional 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 demosaicing and 4D light field view synthesis. | |
dc.description.sponsorship | VCC KAUST Baseline Funding, Terman Faculty Fellowship, Intel Compressive Sensing Alliance, National Science Foundation (IIS 1553333), and NSF/Intel Partnership on Visual and Experiential Computing (IIS 1539120). | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
dc.relation.url | http://ieeexplore.ieee.org/document/8237721/ | |
dc.subject | Convolutional sparse coding | |
dc.subject | Convex Optimization, Machine Learning, Big Data, Image Reconstruction, High-dimensional Data | |
dc.title | Consensus Convolutional Sparse Coding | |
dc.type | Conference Paper | |
dc.contributor.department | Visual Computing Center, King Abdullah University of Science and Technology (KAUST) | |
dc.identifier.journal | IEEE Xplore | |
dc.conference.date | October 22-29, 2017 | |
dc.conference.name | International Conference on Computer Vision (ICCV) | |
dc.conference.location | Venice, Italy | |
dc.eprint.version | Post-print | |
dc.contributor.institution | University of Toronto, Stanford University | |
refterms.dateFOA | 2018-06-14T05:18:07Z | |
dc.date.published-online | 2017-12-25 | |
dc.date.published-print | 2017-10 |