Compressed Sensing of Generative Sparse-latent (GSL) Signals
Antoine Honoré Anubhab Ghosh Saikat Chatterjee
We consider reconstruction of an ambient signal in a compressed sensing (CS)
setup where the ambient signal has a neural network based generative model. The
generative model has a sparse-latent input and we refer to the generated
ambient signal as generative sparse-latent signal (GSL). The proposed sparsity
inducing reconstruction algorithm is inherently non-convex, and we show that a
gradient based search provides a good reconstruction performance. We evaluate
our proposed algorithm using simulated data.