# Enhancing Model Interpretability with Local Attribution over Global Exploration

arXiv: https://arxiv.org/abs/2408.07736 · HTML: https://mlsanity.com/paper/2408.07736/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Guided Integrated Gradients: An Adaptive Path Method for Removing Noise](https://mlsanity.com/paper/2106.09788/) (2021)
- [Fast Axiomatic Attribution for Neural Networks](https://mlsanity.com/paper/2111.07668/) (2021)
- [Robust Models Are More Interpretable Because Attributions Look Normal](https://mlsanity.com/paper/2103.11257/) (2021)
- [Stability Guarantees for Feature Attributions with Multiplicative Smoothing](https://mlsanity.com/paper/2307.05902/) (2023)
- [MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks](https://mlsanity.com/paper/2312.13630/) (2023)
- [Artificial Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges](https://mlsanity.com/paper/2108.03383/) (2021)

## Built on by (most cited first, 4 of 4)

- [Beyond Patches: Mining Interpretable Part-Prototypes for Explainable AI](https://mlsanity.com/paper/2504.12197/) (2025)
- [Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial Attacks](https://mlsanity.com/paper/2408.12670/) (2024)
- [Attribution for Enhanced Explanation with Transferable Adversarial eXploration](https://mlsanity.com/paper/2412.19523/) (2024)
- [ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability](https://mlsanity.com/paper/2505.06258/) (2025)

## Abstract (arXiv)

In the field of artificial intelligence, AI models are frequently described as `black boxes' due to the obscurity of their internal mechanisms. It has ignited research interest on model interpretability, especially in attribution methods that offers precise explanations of model decisions. Current attribution algorithms typically evaluate the importance of each parameter by exploring the sample space. A large number of intermediate states are introduced during the exploration process, which may reach the model's Out-of-Distribution (OOD) space. Such intermediate states will impact the attribution results, making it challenging to grasp the relative importance of features. In this paper, we firstly define the local space and its relevant properties, and we propose the Local Attribution (LA) algorithm that leverages these properties. The LA algorithm comprises both targeted and untargeted exploration phases, which are designed to effectively generate intermediate states for attribution that thoroughly encompass the local space. Compared to the state-of-the-art attribution methods, our approach achieves an average improvement of 38.21% in attribution effectiveness. Extensive ablation studies in our experiments also validate the significance of each component in our algorithm. Our code is available at: https://github.com/LMBTough/LA/