arXiv:2605.24008v1 Announce Type: new 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 whil
The increasing complexity and deployment of AI in critical applications necessitate robust fault detection methods, with scalability becoming a key constraint for real-world integration.
Improved and scalable fault detection mechanisms for DNNs are crucial for the widespread and safe adoption of AI, particularly in high-stakes environments.
The introduction of CAFD offers a more efficient and scalable method for identifying faults in deep neural networks, potentially accelerating their reliability and deployment.
- · AI developers
- · Industries deploying AI (e.g., autonomous systems, healthcare)
- · AI safety researchers
- · Companies relying on computationally intensive fault detection methods
- · Those struggling with AI reliability issues
Wider adoption of deep neural networks in critical applications due to enhanced reliability.
Increased investment in AI certification and validation processes, fostering industry standards.
A potential reduction in regulatory hurdles for advanced AI systems as their safety and predictability improve.
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Read at arXiv cs.LG