  To interpret deep networks, one main approach is to associate neurons with
human-understandable concepts. However, existing methods often ignore the
inherent relationships of different concepts (e.g., dog and cat both belong to
animals), and thus lose the chance to explain neurons responsible for
higher-level concepts (e.g., animal). In this paper, we study hierarchical
concepts inspired by the hierarchical cognition process of human beings. To
this end, we propose HIerarchical Neuron concepT explainer (HINT) to
effectively build bidirectional associations between neurons and hierarchical
concepts in a low-cost and scalable manner. HINT enables us to systematically
and quantitatively study whether and how the implicit hierarchical
relationships of concepts are embedded into neurons, such as identifying
collaborative neurons responsible to one concept and multimodal neurons for
different concepts, at different semantic levels from concrete concepts (e.g.,
dog) to more abstract ones (e.g., animal). Finally, we verify the faithfulness
of the associations using Weakly Supervised Object Localization, and
demonstrate its applicability in various tasks such as discovering saliency
regions and explaining adversarial attacks. Code is available on
https://github.com/AntonotnaWang/HINT.
