CAFD improves DNN fault detection by 18.3% on average using a novel concept-based feature from VLMs alongside other signals, outperforming five baselines on multiple models and datasets.
A highly efficient diversity-based input selection for dnn improvement using vlms,
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CAFD: Concept-Aware DNN Fault Detection using VLMs
CAFD improves DNN fault detection by 18.3% on average using a novel concept-based feature from VLMs alongside other signals, outperforming five baselines on multiple models and datasets.