CAFD: Concept-Aware DNN Fault Detection using VLMs
This paper introduces CAFD, a learning-based approach that integrates model-based signals, distance features, and a novel Concept Failure Ratio (CFR) feature extracted via Vision-Language Models to achieve superior fault detection performance while maintaining efficiency, with an average 18.3% FDR improvement over state-of-the-art baselines.
Article intelligence
Key points
- CAFD is a lightweight learning-based method that effectively combines multiple information sources for DNN fault detection
- It introduces Concept Failure Ratio (CFR), a novel feature leveraging VLMs to extract semantic concepts from images
- Extensive evaluations on ImageNet and other datasets show CAFD outperforms five baselines by 18.3% average FDR improvement
Why it matters
This matters because CAFD is a lightweight learning-based method that effectively combines multiple information sources for DNN fault detection.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.24008] CAFD: Concept-Aware DNN Fault Detection using VLMs
[Submitted on 19 May 2026]
Title:CAFD: Concept-Aware DNN Fault Detection using VLMs
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Abstract:Fault detection for Deep Neural Networks (DNNs) has received increasing attention in recent years. While more advanced hybrid approaches have been proposed to combine multiple sources of information and outperform earlier techniques, they often incur substantial computational overhead, limiting scalability and practicality in real-world settings. In this paper, we introduce Concept-Aware Fault Detection (CAFD), a learning-based approach that achieves superior fault detection performance by effectively integrating multiple information sources while maintaining practical efficiency. Specifically, CAFD is trained using a carefully selected set of informative features, including model-based signals derived from the DNN's outputs, distance-based features, and a novel concept-based feature, called Concept Failure Ratio (CFR). CFR leverages Vision-Language Models (VLMs) to extract textual concepts from images and quantify the likelihood that their presence is associated with DNN failures. By incorporating this feature, CAFD benefits from complementary semantic information, enabling more effective fault detection. Our results demonstrate that CFR serves as an effective indicator for DNN fault detection. We conduct an extensive empirical evaluation of CAFD, comparing it against five state-of-the-art baselines across three subject DNN models and datasets, including ImageNet. Across a wide range of constrained selection budgets, CAFD consistently outperforms all baselines in Fault Detection Rate (FDR), achieving average FDR improvements of 18.3% across all investigated subjects and budget sizes.
Subjects:
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Software Engineering (cs.SE)
Cite as: arXiv:2605.24008 [cs.LG]
(or arXiv:2605.24008v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.24008
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mahboubeh Dadkhah [view email] [v1] Tue, 19 May 2026 16:14:13 UTC (165 KB)
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