Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Models. (arXiv:2207.01916v1 [cs.CV])

Explanations for \emph{black-box} models help us understand model decisions
as well as provide information on model biases and inconsistencies. Most of the
current explainability techniques provide a single level of explanation, often
in terms of feature importance scores or feature attention maps in input space.
Our focus is on explaining deep discriminative models at \emph{multiple levels
of abstraction}, from fine-grained to fully abstract explanations. We achieve
this by using the natural properties of \emph{hyperbolic geometry} to more
efficiently model a …

arxiv cv hierarchical reasoning space symbolic reasoning