A wide range of verification methods have been proposed to verify the safety
properties of deep neural networks ensuring that the networks function
correctly in critical applications. However, many well-known verification tools
still struggle with complicated network architectures and large network sizes.
In this work, we propose a network reduction technique as a pre-processing
method prior to verification. The proposed method reduces neural networks via
eliminating stable ReLU neurons, and transforming them into a sequential neural
network consisting of ReLU and Affine layers which can be handled by the most
verification tools. We instantiate the reduction technique on the
state-of-the-art complete and incomplete verification tools, including
alpha-beta-crown, VeriNet and PRIMA. Our experiments on a large set of
benchmarks indicate that the proposed technique can significantly reduce neural
networks and speed up existing verification tools. Furthermore, the experiment
results also show that network reduction can improve the availability of
existing verification tools on many networks by reducing them into sequential
neural networks.

通过消除稳定的 ReLU 神经元并将其转化为由 ReLU 和仿射层组成的顺序神经网络，我们提出了一种网络削减技术作为预处理方法以便于验证，实验证明这种技术能够显著减小神经网络规模并加快验证过程。