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cs.CV2025
Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations
Dahee Kwon, Sehyun Lee, Jaesik Choi
Deep vision models have achieved remarkable classification performance by leveraging a hierarchical architecture in which human-interpretable concepts emerge through the compositio…
cs.LG2025
Pathwise Explanation of ReLU Neural Networks
Seongwoo Lim, Won Jo, Joohyung Lee +1
Neural networks have demonstrated a wide range of successes, but their ``black box" nature raises concerns about transparency and reliability. Previous research on ReLU networks ha…
cs.LG2025★ 1 cited
Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information
Youngju Joung, Sehyun Lee, Jaesik Choi
To improve trust and transparency, it is crucial to be able to interpret the decisions of Deep Neural classifiers (DNNs). Instance-level examinations, such as attribution technique…