ICON: Learning Regular Maps Through Inverse Consistency.

ABSTRACT: Learning maps between data samples is fundamental. Applications range from representation learning, image translation and generative modeling, to the estimation of spatial deformations. Such maps relate feature vectors, or map between feature spaces. Well-behaved maps should be regular, which can be imposed explicitly or may emanate from the data itself. We explore what induces regularity for spatial transformations, e.g., when computing image registrations. Classical optimization-based models compute maps between pairs of samples and rely on an appropriate regularizer for well-posedness. Recent deep learning approaches have attempted to avoid using such regularizers altogether by relying on the sample population instead. We explore if it is possible to obtain spatial regularity using an i

SUBMITTER: Greer H 

PROVIDER: S-EPMC8963462 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature