Quickest Change Detection with Diffusion-Integrated Scores
DI-SCUSUM is a training-free change detection method that integrates score evidence across various Gaussian smoothing scales. It uses diffusion to sample perturbations and calculate importance-weighted scores, effectively recovering directed Kullback-Leibler divergences. This approach achieves superior detection delay bounds compared to single-scale methods, showing significant performance gains in simulations and experiments.