Online Convolutional Dictionary Learning
This work addresses a memory bottleneck for researchers and practitioners handling large datasets in convolutional dictionary learning, though it is incremental as it extends existing online methods to a new context.
The paper tackles the high memory cost of convolutional dictionary learning by proposing an online algorithm that processes data incrementally, reducing memory requirements that previously grew linearly with dataset size.
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.