  Deep learning based techniques achieve state-of-the-art results in a wide
range of image reconstruction tasks like compressed sensing. These methods
almost always have hyperparameters, such as the weight coefficients that
balance the different terms in the optimized loss function. The typical
approach is to train the model for a hyperparameter setting determined with
some empirical or theoretical justification. Thus, at inference time, the model
can only compute reconstructions corresponding to the pre-determined
hyperparameter values. In this work, we present a hypernetwork-based approach,
called HyperRecon, to train reconstruction models that are agnostic to
hyperparameter settings. At inference time, HyperRecon can efficiently produce
diverse reconstructions, which would each correspond to different
hyperparameter values. In this framework, the user is empowered to select the
most useful output(s) based on their own judgement. We demonstrate our method
in compressed sensing, super-resolution and denoising tasks, using two
large-scale and publicly-available MRI datasets. Our code is available at
https://github.com/alanqrwang/hyperrecon.
