AdaStop: Cost-Aware Early Stopping for DNN Test Selection
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arXiv:2607.05461v1 Announce Type: new 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…
1Key Takeaways
- arXiv:2607.05461v1 Announce Type: new 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….
2AIWedia Score
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv ML reports that arXiv:2607.05461v1 Announce Type: new Abstract: Existing methods for testing deep neural networks (DNNs) primarily prioritize test inputs likely to reveal model faults under a fixed labeling budget.
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