Reproducibility Companion Paper: Enhancing Model Interpretability with Local Attribution over Global Exploration

摘要

Reproducibility is indispensable for transferring explainable-AI algorithms from academic prototypes to production systems. This companion paper documents the artefacts, procedures, and outcomes that reproduce the empirical claims of ''Enhancing Model Interpretability with Local Attribution over Global Exploration'' (ACM MM 2024). We release a containerised archive containing source code, data-serialisation scripts, one-click executables, and a detailed README, all conforming to the ACM Multimedia reproducibility guidelines. The regenerated Insertion and Deletion scores deviate by only 2.2% on average. In addition, an exhaustive 10, 20, 30 3 grid-search over key hyper-parameters reveals a new configuration, (30, 20, 30), that improves the Insertion score of three convolutional backbones by 7.51% without additional code changes. These artefacts provide a rigorous, extensible foundation for future research on local attribution methods. Our code is available at: https://github.com/LMBTough/LA/