Deep generative demixing: Recovering Lipschitz signals from noisy subgaussian mixtures
This work addresses signal demixing for applications in imaging and data analysis, but it is incremental as it builds on existing theoretical frameworks.
The paper tackles the problem of recovering two Lipschitz signals from their noisy subgaussian mixture, extending prior compressed sensing results to non-convex settings like generative neural networks, and proves a sample complexity bound for nearly optimal recovery error.
Generative neural networks (GNNs) have gained renown for efficaciously capturing intrinsic low-dimensional structure in natural images. Here, we investigate the subgaussian demixing problem for two Lipschitz signals, with GNN demixing as a special case. In demixing, one seeks identification of two signals given their sum and prior structural information. Here, we assume each signal lies in the range of a Lipschitz function, which includes many popular GNNs as a special case. We prove a sample complexity bound for nearly optimal recovery error that extends a recent result of Bora, et al. (2017) from the compressed sensing setting with gaussian matrices to demixing with subgaussian ones. Under a linear signal model in which the signals lie in convex sets, McCoy & Tropp (2014) have characterized the sample complexity for identification under subgaussian mixing. In the present setting, the signal structure need not be convex. For example, our result applies to a domain that is a non-convex union of convex cones. We support the efficacy of this demixing model with numerical simulations using trained GNNs, suggesting an algorithm that would be an interesting object of further theoretical study.