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We study how stochastic differential equation (SDE) based ideas can inspirenew modifications to existing algorithms for a set of problems in computervision. Loosely speaking, our formulation is related to both explicit andimplicit strategies for data augmentation and group equivariance, but isderived from new results in the SDE literature on estimating infinitesimalgenerators of a class of stochastic processes. If and when there is nominalagreement between the needs of an application/task and the inherent propertiesand behavior of the types of processes that we can efficiently handle, weobtain a very simple and efficient plug-in layer that can be incorporatedwithin any existing network architecture, with minimal modification and only afew additional parameters. We show promising experiments on a number of visiontasks including few shot learning, point cloud transformers and deepvariational segmentation obtaining efficiency or performance improvements.

Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators