  Deep Neural Networks (DNNs) demonstrate remarkable capabilities in learning
complex hierarchical data representations, but the nature of these
representations remains largely unknown. Existing global explainability
methods, such as Network Dissection, face limitations such as reliance on
segmentation masks, lack of statistical significance testing, and high
computational demands. We propose Inverse Recognition (INVERT), a scalable
approach for connecting learned representations with human-understandable
concepts by leveraging their capacity to discriminate between these concepts.
In contrast to prior work, INVERT is capable of handling diverse types of
neurons, exhibits less computational complexity, and does not rely on the
availability of segmentation masks. Moreover, INVERT provides an interpretable
metric assessing the alignment between the representation and its corresponding
explanation and delivering a measure of statistical significance. We
demonstrate the applicability of INVERT in various scenarios, including the
identification of representations affected by spurious correlations, and the
interpretation of the hierarchical structure of decision-making within the
models.
