ExLipBaB: Exact Lipschitz Constant Computation for Piecewise Linear Neural Networks
Tom A. Splittgerber

TL;DR
This paper introduces ExLipBaB, an algorithm for exact Lipschitz constant computation applicable to various piecewise linear neural networks, enhancing accuracy in robustness and generalization assessments.
Contribution
It generalizes the LipBaB algorithm to handle arbitrary piecewise linear activations and norms, enabling exact Lipschitz constant computation beyond ReLU networks.
Findings
Supports diverse activations like ReLU, LeakyReLU, GroupSort, MaxPool.
Applicable to different p-norms for Lipschitz calculations.
Facilitates precise robustness guarantees and benchmarking.
Abstract
It has been shown that a neural network's Lipschitz constant can be leveraged to derive robustness guarantees, to improve generalizability via regularization or even to construct invertible networks. Therefore, a number of methods varying in the tightness of their bounds and their computational cost have been developed to approximate the Lipschitz constant for different classes of networks. However, comparatively little research exists on methods for exact computation, which has been shown to be NP-hard. Nonetheless, there are applications where one might readily accept the computational cost of an exact method. These applications could include the benchmarking of new methods or the computation of robustness guarantees for small models on sensitive data. Unfortunately, existing exact algorithms restrict themselves to only ReLU-activated networks, which are known to come with severe…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Stochastic Gradient Optimization Techniques · Advanced Graph Neural Networks
