CoPISan: Contrastive Perceptual Inference and Sanity Checks for Concept-Based CNN Explanations

Despite the effectiveness of convolutional neural networks (CNNs) in visual categorization, the logic behind their predictions is not human-understandable. While existing concept-based explainability methods reveal what a CNN sees, there is a need to understand how a specific concept is chosen (rather than another concept) for a prediction, aligning more closely with human perception. To address this challenge, we propose a novel contrastive paradigm to bridge the critical gap in global concept discovery by leveraging contrasts from cognitive sciences for discriminative concept retrieval. A new multiple-case concept retrieval method is proposed for improved local understanding of (dis)similar classification cases. We argue that a contrastive paradigm for concept retrieval and sanity checks is essential to an explainer's trustworthiness and integrate these missing ingredients into state-of-the-art concept-based explanation frameworks to foster a better human understanding through contrast. The proposed Contrastive Perceptual Inference and Sanity Checks for Concept-based CNN Explanations (CoPISan) framework accelerates salient concept retrieval. It evaluates explainer trustworthiness via sanity checks conducted under Frontdoor and Poisoning adversarial attacks. Experimental results demonstrate CoPISan's encouraging performance, mitigating issues related to duplication, entanglement, diminishing returns, and ambiguity of concept explanations. CoPISan is motivated by cognition and perception, offers theoretical justification and resilience, and is computationally efficient.