Finkbeiner, Bernd
; Kovacs, Laura
(Ed.)
With the growing use of deep neural networks(DNN) in mis-
sion and safety-critical applications, there is an increasing interest in
DNN verification. Unfortunately, increasingly complex network struc-
tures, non-linear behavior, and high-dimensional input spaces combine
to make DNN verification computationally challenging. Despite tremen-
dous advances, DNN verifiers are still challenged to scale to large ver-
ification problems. In this work, we explore how the number of stable
neurons under the precondition of a specification gives rise to verifica-
tion complexity. We examine prior work on the problem, adapt it, and
develop several novel approaches to increase stability. We demonstrate
that neuron stability can be increased substantially without compromis-
ing model accuracy and this yields a multi-fold improvement in DNN
verifier performance.
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