  We introduce Discovering Conceptual Network Explanations (DCNE), a new
approach for generating human-comprehensible visual explanations to enhance the
interpretability of deep neural image classifiers. Our method automatically
finds visual explanations that are critical for discriminating between classes.
This is achieved by simultaneously optimizing three criteria: the explanations
should be few, diverse, and human-interpretable. Our approach builds on the
recently introduced Concept Relevance Propagation (CRP) explainability method.
While CRP is effective at describing individual neuronal activations, it
generates too many concepts, which impacts human comprehension. Instead, DCNE
selects the few most important explanations. We introduce a new evaluation
dataset centered on the challenging task of classifying birds, enabling us to
compare the alignment of DCNE's explanations to those of human expert-defined
ones. Compared to existing eXplainable Artificial Intelligence (XAI) methods,
DCNE has a desirable trade-off between conciseness and completeness when
summarizing network explanations. It produces 1/30 of CRP's explanations while
only resulting in a slight reduction in explanation quality. DCNE represents a
step forward in making neural network decisions accessible and interpretable to
humans, providing a valuable tool for both researchers and practitioners in XAI
and model alignment.
