A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding

Understand

We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems, and whose architectures are derived from an optimization algorithm.

• We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions.
• This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end.
• The priors used in this presentation include variants of total variation, Laplacian regularization, bilateral filtering, sparse coding on learned dictionaries, and non-local self similarities.