Verifying the behavior of neural networks is necessary if developers are to confidently deploy them as parts of mission-critical systems. Toward this end, researchers have been actively developing a range of increasingly sophisticated and scalable neural network verifiers. However, scaling verification to large networks is challenging, at least in part due to the significant memory requirements of verification algorithms. In this paper, we propose an assume-guarantee compositional framework, CoVeNN, that is parameterized by an underlying verifier to generate a sequence of verification sub-problems to address this challenge. We present an iterative refinement-based strategy for computing assumptions that allow sub-problems to retain sufficient accuracy. An evaluation using 7 neural networks and a total of 140 property specifications demonstrates that CoVeNN can verify nearly 7 times more problems than state-of-the-art verifiers.

Branch-and-bound Our work is Bbased on branch-and-boundBound (BaB), a powerful framework for -2.0 0.5 Lower bound = min(-2.0,

This is quite similar to what VCT can learn on the synthesized dataset Objects-Room. As the real-world dataset is more diverse, we observe several failure cases shown in Figure 8. We suppose those failure cases are due to VCT, trained withreconstruction loss,isnotgoodatsynthesizing counterfactual samples which arefarfromthe data distribution.