Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations

Deep vision models have achieved remarkable classification performance by leveraging a hierarchical architecture in which human-interpretable concepts emerge through the composition of individual neurons across layers. Given the distributed nature of representations, pinpointing where specific concepts are encoded within a model remains a crucial yet challenging task in computer vision. In this paper, we introduce an effective circuit discovery method, called $\textit{Granular Concept Circuits (GCCs)}$, in which each circuit represents a concept relevant to a given query. Our method iteratively assesses inter-neuron connectivity—focusing on dependencies and semantic alignment—to construct each GCC. By automatically discovering multiple GCCs, each capturing specific concepts within that query, our approach offers a profound, concept-wise interpretation of models and is the first to identify circuits tied to specific visual concepts at a fine-grained level. We validate the versatility and effectiveness of GCCs across various deep image classification models. The source code will be publicly available.