In this paper, we introduce VPNet, a novel model-driven neural network
architecture based on variable projections (VP). The application of VP
operators in neural networks implies learnable features, interpretable
parameters, and compact network structures. This paper discusses the motivation
and mathematical background of VPNet as well as experiments. The concept was
evaluated in the context of signal processing. We performed classification
tasks on a synthetic dataset, and real electrocardiogram (ECG) signals.
Compared to fully-connected and 1D convolutional networks, VPNet features fast
learning ability and good accuracy at a low computational cost in both of the
training and inference. Based on the promising results and mentioned
advantages, we expect broader impact in signal processing, including
classification, regression, and even clustering problems
