  A pre-trained generator has been frequently adopted in compressed sensing
(CS) due to its ability to effectively estimate signals with the prior of NNs.
In order to further refine the NN-based prior, we propose a framework that
allows the generator to utilize additional information from a given measurement
for prior learning, thereby yielding more accurate prediction for signals. As
our framework has a simple form, it is easily applied to existing CS methods
using pre-trained generators. We demonstrate through extensive experiments that
our framework exhibits uniformly superior performances by large margin and can
reduce the reconstruction error up to an order of magnitude for some
applications. We also explain the experimental success in theory by showing
that our framework can slightly relax the stringent signal presence condition,
which is required to guarantee the success of signal recovery.
