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AdaStop：成本意识的深度神经网络测试选择早停框架AdaStop: Cost-Aware Early Stopping for DNN Test Selection

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以下内容为 AI 自动翻译，英文原文附后供参考。

摘要

研究人员推出了AdaStop，这是一种新颖的框架，旨在优化深度神经网络（DNN）测试中的早停过程。该框架战略性地平衡了标记数据的成本与故障发现的速度，从而实现更高效的测试过程。通过实施AdaStop，组织可以显著减少标记工作，同时保持DNN故障识别的有效性。随着DNN复杂性的不断增长，这一进展尤为重要，迫切需要更高效的测试方法。

Researchers have introduced AdaStop, a novel framework designed to optimize the early stopping process in deep neural network (DNN) testing. This framework strategically balances the costs associated with labeling data against the rates of fault discovery, leading to a more efficient testing process. By implementing AdaStop, organizations can significantly reduce their labeling efforts while maintaining the effectiveness of fault identification in DNNs. This advancement is particularly relevant as the complexity of DNNs continues to grow, necessitating more efficient testing methodologies.

要点

  1. AdaStop通过平衡成本和故障发现来优化DNN测试中的早停。

    AdaStop optimizes early stopping in DNN testing by balancing costs and fault discovery.

  2. 该框架显著减少了标记工作，同时保持故障识别的有效性。

    The framework significantly reduces labeling efforts while maintaining fault identification effectiveness.

  3. 这项研究解决了DNN日益增长的复杂性和对高效测试解决方案的需求。

    This research addresses the growing complexity of DNNs and the need for efficient testing solutions.

为何重要

AdaStop的开发标志着机器学习中更高效测试实践的重要一步，尤其是在DNN日益复杂的情况下。这可以加快部署时间并降低AI项目的成本。

The development of AdaStop signifies a crucial step towards more efficient testing practices in machine learning, especially as DNNs become increasingly complex. This can lead to faster deployment times and reduced costs in AI projects.

按谁的角度看

产品经理

对于产品经理而言，AdaStop提供了一种简化测试流程的方法，可能导致更快的产品迭代和降低与数据标记相关的运营成本。

For product managers, AdaStop offers a way to streamline testing processes, potentially leading to quicker product iterations and reduced operational costs associated with labeling data.

开发者

开发人员和工程师可以利用AdaStop来增强他们的测试框架，从而在测试阶段实现更高效的故障检测和减少资源分配。

Developers and engineers can leverage AdaStop to enhance their testing frameworks, allowing for more efficient fault detection and reduced resource allocation during the testing phase.

学生与求职者

在AI领域的学生和求职者可以从理解AdaStop的方法中受益，因为它突显了在DNN中成本效益测试策略的重要性，这在不断发展的就业市场中是一项宝贵技能。

Students and job-seekers in the AI field can benefit from understanding AdaStop's approach, as it highlights the importance of cost-effective testing strategies in DNNs, a valuable skill in the evolving job market.

覆盖来源

来源标题与摘录保留原文，以便核对与追溯。

  • arXiv cs.LG

    AdaStop: Cost-Aware Early Stopping for DNN Test Selection

    Researchers developed AdaStop, a framework that optimizes early stopping in deep neural network testing by balancing labeling costs with fault discovery rates. This approach allows for significant reductions in labeling efforts while still effectively identifying faults.