  Convolutional sparse coding (CSC) has been popularly used for the learning of
shift-invariant dictionaries in image and signal processing. However, existing
methods have limited scalability. In this paper, instead of convolving with a
dictionary shared by all samples, we propose the use of a sample-dependent
dictionary in which filters are obtained as linear combinations of a small set
of base filters learned from the data. This added flexibility allows a large
number of sample-dependent patterns to be captured, while the resultant model
can still be efficiently learned by online learning. Extensive experimental
results show that the proposed method outperforms existing CSC algorithms with
significantly reduced time and space requirements.
