Fast and Flexible Convolutional Sparse Coding

Felix Heide, Wolfgang Heidrich, Gordon Wetzstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

154 Scopus citations

Abstract

Convolutional sparse coding (CSC) has become an increasingly important tool in machine learning and computer vision. Image features can be learned and subsequently used for classification and reconstruction tasks. As opposed to patch-based methods, convolutional sparse coding operates on whole images, thereby seamlessly capturing the correlation between local neighborhoods. In this paper, we propose a new approach to solving CSC problems and show that our method converges significantly faster and also finds better solutions than the state of the art. In addition, the proposed method is the first efficient approach to allow for proper boundary conditions to be imposed and it also supports feature learning from incomplete data as well as general reconstruction problems.
Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Computer Vision and Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages5135-5143
Number of pages9
ISBN (Print)9781467369640
DOIs
StatePublished - Oct 15 2015

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