论文标题
神经网络的验证：通过修剪增强可扩展性
Verification of Neural Networks: Enhancing Scalability through Pruning
论文作者
论文摘要
深度神经网络的验证目睹了最近的兴趣激增，这是由于各种领域的成功故事以及对设想应用程序安全和保障的并随时担心。此类网络的复杂性和纯粹的规模对于自动化的正式验证技术而言是挑战性的，另一方面，这些技术可以缓解在安全和关键安全环境中采用深网。 在本文中，我们着重于启用最先进的验证工具来处理具有实际兴趣的神经网络。我们提出了基于网络修剪的新培训管道，目的是在保持准确性和鲁棒性之间达到平衡，同时使最终的网络可与正式分析相提并论。我们通过修剪算法和验证工具组合进行实验的结果表明，我们考虑的网络和修剪和验证技术的某些组合是成功的方法，从而使深层神经网络更接近正式接地方法的范围。
Verification of deep neural networks has witnessed a recent surge of interest, fueled by success stories in diverse domains and by abreast concerns about safety and security in envisaged applications. Complexity and sheer size of such networks are challenging for automated formal verification techniques which, on the other hand, could ease the adoption of deep networks in safety- and security-critical contexts. In this paper we focus on enabling state-of-the-art verification tools to deal with neural networks of some practical interest. We propose a new training pipeline based on network pruning with the goal of striking a balance between maintaining accuracy and robustness while making the resulting networks amenable to formal analysis. The results of our experiments with a portfolio of pruning algorithms and verification tools show that our approach is successful for the kind of networks we consider and for some combinations of pruning and verification techniques, thus bringing deep neural networks closer to the reach of formally-grounded methods.
