Seongwoo Lim

2024

Pathwise Explanation of ReLU Neural Networks

We decompose the ReLU neural network into components, called paths, based on their locally linear attributes. We identify a piecewise linear model corresponding to each path, and use it to generate input attributions that explain the model’s decisions. Configuration of the paths allow explanations to be generated by concentrating on the important parts, and decomposed across different paths to explain each object and feature.