Reparameterized Tensor Ring Functional Decomposition for Multi-Dimensional Data Recovery

ArXi:2603.01034v2 Announce Type: replace-cross Tensor Ring (TR) decomposition is a powerful tool for high-order data modeling, but is inherently restricted to discrete forms defined on fixed meshgrids. In this work, we propose a TR functional decomposition for both meshgrid and non-meshgrid data, where factors are parameterized by Implicit Neural Representations (INRs). However, optimizing this continuous framework to capture fine-scale details is intrinsically difficult.