  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.
