  We study how stochastic differential equation (SDE) based ideas can inspire
new modifications to existing algorithms for a set of problems in computer
vision. Loosely speaking, our formulation is related to both explicit and
implicit strategies for data augmentation and group equivariance, but is
derived from new results in the SDE literature on estimating infinitesimal
generators of a class of stochastic processes. If and when there is nominal
agreement between the needs of an application/task and the inherent properties
and behavior of the types of processes that we can efficiently handle, we
obtain a very simple and efficient plug-in layer that can be incorporated
within any existing network architecture, with minimal modification and only a
few additional parameters. We show promising experiments on a number of vision
tasks including few shot learning, point cloud transformers and deep
variational segmentation obtaining efficiency or performance improvements.
