# Fidelity of Ensemble Aggregation for Saliency Map Explanations using Bayesian Optimization Techniques

arXiv: https://arxiv.org/abs/2207.01565 · HTML: https://mlsanity.com/paper/2207.01565/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 44 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Evaluating the visualization of what a Deep Neural Network has learned](https://mlsanity.com/paper/1509.06321/) (2015)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [Top-down Neural Attention by Excitation Backprop](https://mlsanity.com/paper/1608.00507/) (2016)
- [On the (In)fidelity and Sensitivity for Explanations](https://mlsanity.com/paper/1901.09392/) (2019)
- [Evaluating and Aggregating Feature-based Model Explanations](https://mlsanity.com/paper/2005.00631/) (2020)
- [The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective](https://mlsanity.com/paper/2202.01602/) (2022)
- [Explainable Deep Learning: A Field Guide for the Uninitiated](https://mlsanity.com/paper/2004.14545/) (2020)
- [L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data](https://mlsanity.com/paper/1808.02610/) (2018)
- [IROF: a low resource evaluation metric for explanation methods](https://mlsanity.com/paper/2003.08747/) (2020)
- [A Review on Explainability in Multimodal Deep Neural Nets](https://mlsanity.com/paper/2105.07878/) (2021)
- [A simple defense against adversarial attacks on heatmap explanations](https://mlsanity.com/paper/2007.06381/) (2020)
- [iGOS++: Integrated Gradient Optimized Saliency by Bilateral Perturbations](https://mlsanity.com/paper/2012.15783/) (2020)
- [Aggregating explanation methods for stable and robust explainability](https://mlsanity.com/paper/1903.00519/) (2019)
- [A Unified Study of Machine Learning Explanation Evaluation Metrics](https://mlsanity.com/paper/2203.14265/) (2022)
- [Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization](https://mlsanity.com/paper/1807.01279/) (2018)
- [A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines](https://mlsanity.com/paper/2111.07379/) (2021)

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

- [PCIM: Learning Pixel Attributions via Pixel-wise Channel Isolation Mixing in High Content Imaging](https://mlsanity.com/paper/2412.02275/) (2024)

## Abstract (arXiv)

In recent years, an abundance of feature attribution methods for explaining neural networks have been developed. Especially in the field of computer vision, many methods for generating saliency maps providing pixel attributions exist. However, their explanations often contradict each other and it is not clear which explanation to trust. A natural solution to this problem is the aggregation of multiple explanations. We present and compare different pixel-based aggregation schemes with the goal of generating a new explanation, whose fidelity to the model's decision is higher than each individual explanation. Using methods from the field of Bayesian Optimization, we incorporate the variance between the individual explanations into the aggregation process. Additionally, we analyze the effect of multiple normalization techniques on ensemble aggregation.