CVPR 2023, highlight paper - SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries

In this episode we discuss SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries by Ahmed Imtiaz Humayun, Randall Balestriero, Guha Balakrishnan, Richard Baraniuk. This paper presents a new method called SplineCam that enables exact computation of the geometry of a deep network's (DN) mapping, including its decision boundary, without resorting to approximations such as sampling or architecture simplification. SplineCam works for any DN architecture based on Continuous Piece-Wise Linear (CPWL) nonlinearities and can be used for regression DNs as well. This method facilitates comparison of architectures, generalizability measurement, and sampling from the decision boundary on or off the manifold.