Deep neural network (DNN) verification is an emerging field, with diverse verification engines quickly becoming available. Demonstrating the effectiveness of these engines on real-world DNNs is an important step towards their wider adoption. We present a tool that can leverage existing verification engines in performing a novel application: neural network simplification, through the reduction of the size of a DNN without harming its accuracy. We report on the work-flow of the simplification process, and demonstrate its potential significance and applicability on a family of real-world DNNs for aircraft collision avoidance, whose sizes we were able to reduce by as much as 10%.
CITATION STYLE
Gokulanathan, S., Feldsher, A., Malca, A., Barrett, C., & Katz, G. (2020). Simplifying Neural Networks Using Formal Verification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12229 LNCS, pp. 85–93). Springer. https://doi.org/10.1007/978-3-030-55754-6_5
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