First three years of the international verification of neural networks competition (VNN-COMP)
First three years of the international verification of neural networks competition (VNN-COMP)
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DOI：
10.1007/s10009-023-00703-4
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发表时间：
2023-01
影响因子：
1.5
通讯作者：
Christopher Brix;Mark Niklas Muller;Stanley Bak;Taylor T. Johnson;Changliu Liu
中科院分区：
文献类型：
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作者：
Christopher Brix;Mark Niklas Muller;Stanley Bak;Taylor T. Johnson;Changliu Liu
本文对2020年、2021年和2022年举行的年度国际神经网络验证竞赛（VNN-COMP）的前三次迭代进行了总结和荟萃分析。在VNN-COMP中，参与者提交软件工具来分析给定的神经网络是否满足描述其输入-输出行为的规范。这些神经网络和规范涵盖了各种各样的问题类别和任务，对应于图像分类、神经控制、强化学习和自主系统中的安全性和鲁棒性。我们总结了过去三年中观察到的关键过程、规则和结果、目前的趋势，并对未来可能的发展进行了展望。
This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP), held in 2020, 2021, and 2022. In the VNN-COMP, participants submit software tools that analyze whether given neural networks satisfy specifications describing their input-output behavior. These neural networks and specifications cover a variety of problem classes and tasks, corresponding to safety and robustness properties in image classification, neural control, reinforcement learning, and autonomous systems. We summarize the key processes, rules, and results, present trends observed over the last three years, and provide an outlook into possible future developments.