  In this work, we investigated the application of score-based gradient
learning in discriminative and generative classification settings. Score
function can be used to characterize data distribution as an alternative to
density. It can be efficiently learned via score matching, and used to flexibly
generate credible samples to enhance discriminative classification quality, to
recover density and to build generative classifiers. We analysed the decision
theories involving score-based representations, and performed experiments on
simulated and real-world datasets, demonstrating its effectiveness in achieving
and improving binary classification performance, and robustness to
perturbations, particularly in high dimensions and imbalanced situations.
