Online Convolutional Dictionary Learning

Abstract

While a number of different algorithms have recently been proposed for convolutional dictionary learning, this remains an expensive problem. The single biggest impediment to learning from large training sets is the memory requirements, which grow at least linearly with the size of the training set since all existing methods are batch algorithms. The work reported here addresses this limitation by extending online dictionary learning ideas to the convolutional context.

Accepted to be presented at ICIP 2017

Cited by in corpus (7)

• Convolutional Dictionary Learning: A Comparative Review and New Algorithms
• Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning
• Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration
• Convolutional Analysis Operator Learning: Acceleration and Convergence
• Scalable Online Convolutional Sparse Coding
• A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model
• Online Convolutional Sparse Coding with Sample-Dependent Dictionary