ABSTRACT Convolutional sparse coding (CSC) using learnt convolutional dictionaries has recently emerged as an effective technique for emphasising discriminative structures in signal and image processing applications. In this paper, we propose a multilayer model for convolutional sparse networks (CSNs), based on hierarchical convolutional sparse coding and dictionary learning, as a competitive alternative to conventional deep convolutional neural networks (CNNs). In the proposed CSN architecture, each layer learns a convolutional dictionary from the feature maps of the preceding layer (if available), and then uses it to extract sparse representations. This hierarchical process is repeated to obtain high‐level feature maps in the final layer, suitable for pattern recognition and classification tasks. One key advantage of the CSN framework is its reduced sensitivity to training set size and its significantly lower computational complexity compared to CNNs. Experimental results on image classification tasks show that the proposed model achieves up to 7% higher accuracy than CNNs when trained with only 150 samples, while reducing computational cost by at least 50% under similar conditions.
Almalou et al. (2026) studied this question.