# A New Baseline Assumption of Integated Gradients Based on Shaply value

arXiv: https://arxiv.org/abs/2310.04821 · HTML: https://mlsanity.com/paper/2310.04821/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 7 Oct 2023.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [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)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Interpretation of Neural Networks is Fragile](https://mlsanity.com/paper/1710.10547/) (2017)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Real Time Image Saliency for Black Box Classifiers](https://mlsanity.com/paper/1705.07857/) (2017)
- [Problems with Shapley-value-based explanations as feature importance measures](https://mlsanity.com/paper/2002.11097/) (2020)
- [Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers](https://mlsanity.com/paper/1604.00825/) (2016)
- [Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation](https://mlsanity.com/paper/1903.10992/) (2019)
- [Algorithms to estimate Shapley value feature attributions](https://mlsanity.com/paper/2207.07605/) (2022)
- [Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models](https://mlsanity.com/paper/2011.01625/) (2020)
- [Sampling Permutations for Shapley Value Estimation](https://mlsanity.com/paper/2104.12199/) (2021)
- [Shapley explainability on the data manifold](https://mlsanity.com/paper/2006.01272/) (2020)
- [Did the Model Understand the Question?](https://mlsanity.com/paper/1805.05492/) (2018)
- [Sequential Integrated Gradients: a simple but effective method for explaining language models](https://mlsanity.com/paper/2305.15853/) (2023)
- [Explaining a Series of Models by Propagating Shapley Values](https://mlsanity.com/paper/2105.00108/) (2021)
- [Explaining Models by Propagating Shapley Values of Local Components](https://mlsanity.com/paper/1911.11888/) (2019)
- [Quantitative Evaluations on Saliency Methods: An Experimental Study](https://mlsanity.com/paper/2012.15616/) (2020)
- [Maximum Entropy Baseline for Integrated Gradients](https://mlsanity.com/paper/2204.05948/) (2022)
- [Comparing Baseline Shapley and Integrated Gradients for Local Explanation: Some Additional Insights](https://mlsanity.com/paper/2208.06096/) (2022)

## Built on by (most cited first, 3 of 3)

- [Integrated Gradient Correlation: a Dataset-wise Attribution Method](https://mlsanity.com/paper/2404.13910/) (2024)
- [FRInGe: Distribution-Space Integrated Gradients with Fisher--Rao Geometry](https://mlsanity.com/paper/2605.06404/) (2026)
- [OperatorSHAP: Fast and Accurate Shapley Value Estimation for Neural Operators](https://mlsanity.com/paper/2606.28065/) (2026)

## Abstract (arXiv)

Efforts to decode deep neural networks (DNNs) often involve mapping their predictions back to the input features. Among these methods, Integrated Gradients (IG) has emerged as a significant technique. The selection of appropriate baselines in IG is crucial for crafting meaningful and unbiased explanations of model predictions in diverse settings. The standard approach of utilizing a single baseline, however, is frequently inadequate, prompting the need for multiple baselines. Leveraging the natural link between IG and the Aumann-Shapley Value, we provide a novel outlook on baseline design. Theoretically, we demonstrate that under certain assumptions, a collection of baselines aligns with the coalitions described by the Shapley Value. Building on this insight, we develop a new baseline method called Shapley Integrated Gradients (SIG), which uses proportional sampling to mirror the Shapley Value computation process. Simulations conducted in GridWorld validate that SIG effectively emulates the distribution of Shapley Values. Moreover, empirical tests on various image processing tasks show that SIG surpasses traditional IG baseline methods by offering more precise estimates of feature contributions, providing consistent explanations across different applications, and ensuring adaptability to diverse data types with negligible additional computational demand.