arXiv:2607.05461v1 Announce Type: cross Abstract: Existing methods for testing deep neural networks (DNNs) primarily prioritize test inputs likely to reveal model faults under a fixed labeling budget. In practice, choosing that budget is difficult: too little testing misses failures, while too much incurs unnecessary labeling costs. This work studies the stopping problem in DNN testing. We formulate testing as a cost--benefit decision process in which labeling an input incurs cost $c$ and discovering a fault yields value $v$. Based on this formulation, we introduce \textit{AdaStop}, a framewor
The increasing complexity and deployment of Deep Neural Networks (DNNs) necessitates more efficient and cost-effective testing methodologies to ensure reliability and manage resources, making cost-aware solutions timely.
This development allows for more strategic allocation of resources in DNN testing, optimizing the trade-off between identifying faults and incurring labeling costs, which is critical for scalable AI development and deployment.
The testing paradigm for DNNs shifts from fixed budgets to an adaptive, cost-benefit decision process, enabling developers to dynamically adjust testing efforts based on real-time value and cost considerations.
- · AI developers
- · Companies deploying DNNs
- · AI safety researchers
- · MLOps platforms
- · Companies with inefficient testing protocols
- · High-cost data labeling services
More cost-efficient and reliable deployment of deep learning models across various industries.
Reduced barriers to entry for AI development by lowering testing costs, fostering broader AI adoption.
Increased public trust in AI systems due to enhanced reliability and verified performance.
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Read at arXiv cs.AI