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.
翻译:本文件对2020年、2021年和2022年举办的神经网络竞争年度国际核查(VNNN-COMP)头三期进行了总结和元分析。在VNNN-COMP中,参与者提交了软件工具,分析特定神经网络是否符合描述其投入-产出行为的规格。这些神经网络和规格涵盖与图像分类、神经控制、强化学习和自主系统中的安全和稳健性特征相应的各种问题类别和任务。我们总结了关键程序、规则和结果、过去三年所观察到的当前趋势,并对未来可能的发展提出了展望。