Filters learned for two different datasets on the left and right. We show thumbnails of the datasets along with filters learned with the proposed method and with state-of-the-art competing method. In both cases, our method finds a local optimum with an objective that is 3x - 4x lower than comparable methods. (see paper for details).


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.

Paper, Code and Datasets

Paper: [FastFlexibleCSC_Heide2015.pdf (7MB)]
Code and Datasets: [ (91MB)]
Supplement: [ (62MB)]

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