  Deep neural networks have been applied successfully to a wide variety of
inverse problems arising in computational imaging. These networks are typically
trained using a forward model that describes the measurement process to be
inverted, which is often incorporated directly into the network itself.
However, these approaches are sensitive to changes in the forward model: if at
test time the forward model varies (even slightly) from the one the network was
trained for, the reconstruction performance can degrade substantially. Given a
network trained to solve an initial inverse problem with a known forward model,
we propose two novel procedures that adapt the network to a change in the
forward model, even without full knowledge of the change. Our approaches do not
require access to more labeled data (i.e., ground truth images). We show these
simple model adaptation approaches achieve empirical success in a variety of
inverse problems, including deblurring, super-resolution, and undersampled
image reconstruction in magnetic resonance imaging.
