# Axiomatic Attribution for Deep Networks

**Authors:** Mukund Sundararajan, Ankur Taly, Qiqi Yan  
**arXiv:** [1703.01365](https://arxiv.org/abs/1703.01365)  
**Date:** 2017-03-04  
**Summary page:** https://www.summarizepaper.com/en/paper/1703.01365v1/

## Key points

- Authors address the problem of attributing a deep network's prediction to its input features
- Most known attribution methods fail to satisfy two fundamental axioms: Sensitivity and Implementation Invariance
- They design a new attribution method called Integrated Gradients that requires no modification to the original network and is simple to implement using standard gradient operators
- The authors demonstrate the effectiveness of their method by applying it to several image models, text models, and a chemistry model
- Integrated Gradients enables users to debug networks, extract rules from deep networks more effectively, and engage with models in a better way
- This paper provides a valuable contribution to the field of deep learning by identifying the fundamental axioms that attribution methods should satisfy and offers a promising solution for improving the interpretability of deep neural networks.

## Summary

In their paper "Axiomatic Attribution for Deep Networks," authors Mukund Sundararajan, Ankur Taly, and Qiqi Yan address the problem of attributing a deep network's prediction to its input features. They argue that most known attribution methods fail to satisfy two fundamental axioms: Sensitivity and Implementation Invariance. To address this weakness, they design a new attribution method called Integrated Gradients that requires no modification to the original network and is simple to implement using standard gradient operators. The authors demonstrate the effectiveness of their method by applying it to several image models, text models, and a chemistry model. Integrated Gradients enables users to debug networks, extract rules from deep networks more effectively, and engage with models in a better way. By identifying the fundamental axioms that attribution methods should satisfy, this paper provides a valuable contribution to the field of deep learning and offers a promising solution for improving the interpretability of deep neural networks.