AdaStop: Cost-Aware Early Stopping for DNN Test Selection
AdaStop introduces a cost-aware early stopping framework for Deep Neural Network (DNN) test selection, addressing the difficulty of determining optimal labeling budgets. The method formulates testing as a cost-benefit decision process where labeling incurs cost $c$ and fault discovery yields value $v$, stopping when the marginal fault discovery rate drops below the threshold $\tau = c/v$. Empirical results demonstrate significant efficiency gains, allowing the discovery of 65–84% of faults using
Analysis
TL;DR
- AdaStop introduces a cost-aware early stopping framework for Deep Neural Network (DNN) test selection, addressing the difficulty of determining optimal labeling budgets.
- The method formulates testing as a cost-benefit decision process where labeling incurs cost $c$ and fault discovery yields value $v$, stopping when the marginal fault discovery rate drops below the threshold $\tau = c/v$.
- Empirical results demonstrate significant efficiency gains, allowing the discovery of 65–84% of faults using only 9–31% of the total labeling budget.
- The approach is validated across multiple datasets, neural network architectures, and existing test selection strategies.
Why It Matters
This research addresses a critical bottleneck in AI deployment: the high cost of human labeling for model validation. By enabling dynamic determination of when to stop testing based on economic thresholds rather than arbitrary fixed budgets, AdaStop allows practitioners to significantly reduce operational costs while maintaining high confidence in model reliability. This is particularly relevant for industries where labeling resources are scarce or expensive, such as autonomous driving or medical imaging.
Technical Details
- Cost-Benefit Formulation: The core innovation is modeling the testing process not just as accuracy maximization, but as an optimization problem balancing the cost of labeling ($c$) against the value of finding a fault ($v$).
- Marginal Rate Estimation: AdaStop continuously estimates the marginal rate of fault discovery during the testing phase. It dynamically calculates whether the next batch of labeled inputs is likely to yield enough new faults to justify their cost.
- Stopping Criterion: The framework implements a hard stop condition where labeling ceases once the estimated marginal fault discovery rate falls below the ratio $\tau = c/v$. This ensures that resources are not wasted on low-yield testing.
- Generalizability: The method is designed as a framework that can be integrated with various existing DNN test selection strategies, making it adaptable to different model types and evaluation protocols.
Industry Insight
- Resource Optimization: Organizations should adopt dynamic stopping criteria in their QA pipelines to avoid over-testing models that have already reached a point of diminishing returns, potentially cutting labeling costs by up to 70%.
- Economic Alignment: Testing protocols must align with business economics; the value of a detected fault varies by application severity, so the $c/v$ ratio should be customized per use case rather than using a one-size-fits-all approach.
- Scalability for Large Models: As DNNs become larger and more complex, manual labeling becomes prohibitive. AdaStop offers a scalable solution to maintain rigorous testing standards without linearly increasing labor costs.
Disclaimer: The above content is generated by AI and is for reference only.