AI RESEARCH
SCE-LITE-HQ: Smooth visual counterfactual explanations with generative foundation models
arXiv CS.LG
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ArXi:2603.17048v1 Announce Type: new Modern neural networks achieve strong performance but remain difficult to interpret in high-dimensional visual domains. Counterfactual explanations (CFEs) provide a principled approach to interpreting black-box predictions by identifying minimal input changes that alter model outputs. However, existing CFE methods often rely on dataset-specific generative models and incur substantial computational cost, limiting their scalability to high-resolution data.