Algorithm unfolding or unrolling is the technique of constructing a deep
neural network (DNN) from an iterative algorithm. Unrolled DNNs often provide
better interpretability and superior empirical performance over standard DNNs
in signal estimation tasks. An important theoretical question, which has only
recently received attention, is the development of generalization error bounds
for unrolled DNNs. These bounds deliver theoretical and practical insights into
the performance of a DNN on empirical datasets that are distinct from, but
sampled from, the probability density generating the DNN training data. In this
paper, we develop novel generalization error bounds for a class of unrolled
DNNs that are informed by a compound Gaussian prior. These compound Gaussian
networks have been shown to outperform comparative standard and unfolded deep
neural networks in compressive sensing and tomographic imaging problems. The
generalization error bound is formulated by bounding the Rademacher complexity
of the class of compound Gaussian network estimates with Dudley's integral.
Under realistic conditions, we show that, at worst, the generalization error
scales $\mathcal{O}(n\sqrt{\ln(n)})$ in the signal dimension and
$\mathcal{O}(($Network Size$)^{3/2})$ in network size.

通过边界限制 Rademacher 复杂度，我们为一类来自复合高斯先验的展开深度神经网络开发了新颖的泛化误差边界，可在信号维度和网络规模方面进行评估。