A Framework for Evaluating Zero-Shot Image Generation in Concept-based Explainability

ArXi:2605.19855v1 Announce Type: cross Concept-based Explainable Artificial Intelligence (XAI) interprets deep learning models using human-understandable visual features (e.g., textures or object parts) by linking internal representations to class predictions, thereby bridging the gap between low-level image data and high-level semantics. A major challenge, however, is the reliance on large sets of labeled images to represent each concept, which limits scalability.