  Score-based divergences have been widely used in machine learning and
statistics applications. Despite their empirical success, a blindness problem
has been observed when using these for multi-modal distributions. In this work,
we discuss the blindness problem and propose a new family of divergences that
can mitigate the blindness problem. We illustrate our proposed divergence in
the context of density estimation and report improved performance compared to
traditional approaches.
