What is the optimal depth for deep-unfolding architectures at deployment?

Recently, many iterative algorithms proposed for various applications such as compressed sensing, MIMO Detection, etc. have been unfolded and presented as deep networks; these networks are shown to produce better results than the algorithms in their iterative forms. However, deep networks are highly sensitive to the hyperparameters chosen. Especially for a deep unfolded network, using more layers may lead to redundancy and hence, excessive computation during deployment. In this work, we consider the problem of determining the optimal number of layers required for such unfolded architectures. We propose a method that treats the networks as experts and measures the relative importance of the expertise provided by layers using a variant of the popular Hedge algorithm. Based on the importance of the different layers, we determine the optimal layers required for deployment. We study the effectiveness of this method by applying it to two recent and popular deep-unfolding architectures, namely DetNet and TISTANet.