# Pattern-Guided Integrated Gradients

arXiv: https://arxiv.org/abs/2007.10685 · HTML: https://mlsanity.com/paper/2007.10685/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Efficient Explanations from Empirical Explainers](https://mlsanity.com/paper/2103.15429/) (2021)

## Abstract (arXiv)

Integrated Gradients (IG) and PatternAttribution (PA) are two established explainability methods for neural networks. Both methods are theoretically well-founded. However, they were designed to overcome different challenges. In this work, we combine the two methods into a new method, Pattern-Guided Integrated Gradients (PGIG). PGIG inherits important properties from both parent methods and passes stress tests that the originals fail. In addition, we benchmark PGIG against nine alternative explainability approaches (including its parent methods) in a large-scale image degradation experiment and find that it outperforms all of them.