PDE G CNNs

PDE-G-CNNs are a novel class of neural networks that leverage partial differential equations (PDEs) to perform group convolutions, offering a more efficient and interpretable alternative to traditional convolutional neural networks (CNNs). Current research focuses on developing and refining PDE-G-CNN architectures, particularly exploring the use of morphological operators and different types of PDE solvers (e.g., Riemannian and sub-Riemannian) to improve accuracy and geometric interpretability. This approach promises to reduce model complexity, enhance performance, and provide valuable insights into the underlying geometric structures of data, impacting fields requiring efficient and explainable image and signal processing.