  We study generative compressed sensing when the measurement matrix is
randomly subsampled from a unitary matrix (with the DFT as an important special
case). It was recently shown that $\textit{O}(kdn\|
\boldsymbol{\alpha}\|_{\infty}^{2})$ uniformly random Fourier measurements are
sufficient to recover signals in the range of a neural network $G:\mathbb{R}^k
\to \mathbb{R}^n$ of depth $d$, where each component of the so-called local
coherence vector $\boldsymbol{\alpha}$ quantifies the alignment of a
corresponding Fourier vector with the range of $G$. We construct a
model-adapted sampling strategy with an improved sample complexity of
$\textit{O}(kd\| \boldsymbol{\alpha}\|_{2}^{2})$ measurements. This is enabled
by: (1) new theoretical recovery guarantees that we develop for nonuniformly
random sampling distributions and then (2) optimizing the sampling distribution
to minimize the number of measurements needed for these guarantees. This
development offers a sample complexity applicable to natural signal classes,
which are often almost maximally coherent with low Fourier frequencies.
Finally, we consider a surrogate sampling scheme, and validate its performance
in recovery experiments using the CelebA dataset.
