Equivariant neural networks for inverse problems.

ABSTRACT: In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of this approach have motivated a line of research into incorporating other symmetries into deep learning methods, in the form of group equivariant convolutional neural networks. Much of this work has been focused on roto-translational symmetry of R ^d , but other examples are the scaling symmetry of R ^d and rotational symmetry of the sphere. In this work, we demonstrate that group equivariant convolutional operations can naturally be incorporated into learned reconstruction methods for inverse problems that are motivated by the variational regularisation approach. In

SUBMITTER: Celledoni E 

PROVIDER: S-EPMC8317019 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature