  Image recovery from compressive measurements requires a signal prior for the
images being reconstructed. Recent work has explored the use of deep generative
models with low latent dimension as signal priors for such problems. However,
their recovery performance is limited by high representation error. We
introduce a method for achieving low representation error using generators as
signal priors. Using a pre-trained generator, we remove one or more initial
blocks at test time and optimize over the new, higher-dimensional latent space
to recover a target image. Experiments demonstrate significantly improved
reconstruction quality for a variety of network architectures. This approach
also works well for out-of-training-distribution images and is competitive with
other state-of-the-art methods. Our experiments show that test-time
architectural modifications can greatly improve the recovery quality of
generator signal priors for compressed sensing.
