Formal Local Implication Between Two Neural Networks

TL;DR

This paper introduces a formal method to verify if one neural network consistently makes correct decisions whenever another network does within a specific input region, aiding in model comparison and verification.

Contribution

It establishes the foundation and sound formulation for formal local implication between two neural networks over an input region, enabling provable network comparisons.

Findings

Effective in comparing trained and compact networks

Applicable to medical and standard datasets

Provides a sound, formal verification method

Abstract

Given two neural network classifiers with the same input and output domains, our goal is to compare the two networks in relation to each other over an entire input region (e.g., within a vicinity of an input sample). To this end, we establish the foundation of formal local implication between two networks, i.e., N2 implies N1, in an entire input region D. That is, network N1 consistently makes a correct decision every time network N2 does, and it does so in an entire input region D. We further propose a sound formulation for establishing such formally-verified (provably correct) local implications. The proposed formulation is relevant in the context of several application domains, e.g., for comparing a trained network and its corresponding compact (e.g., pruned, quantized, distilled) networks. We evaluate our formulation based on the MNIST, CIFAR10, and two real-world medical datasets, to show its relevance.