  We introduce Optimal Eye Surgeon (OES), a framework for pruning and training
deep image generator networks. Typically, untrained deep convolutional
networks, which include image sampling operations, serve as effective image
priors (Ulyanov et al., 2018). However, they tend to overfit to noise in image
restoration tasks due to being overparameterized. OES addresses this by
adaptively pruning networks at random initialization to a level of
underparameterization. This process effectively captures low-frequency image
components even without training, by just masking. When trained to fit noisy
images, these pruned subnetworks, which we term Sparse-DIP, resist overfitting
to noise. This benefit arises from underparameterization and the regularization
effect of masking, constraining them in the manifold of image priors. We
demonstrate that subnetworks pruned through OES surpass other leading pruning
methods, such as the Lottery Ticket Hypothesis, which is known to be suboptimal
for image recovery tasks (Wu et al., 2023). Our extensive experiments
demonstrate the transferability of OES-masks and the characteristics of
sparse-subnetworks for image generation. Code is available at
https://github.com/Avra98/Optimal-Eye-Surgeon.git.
