A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification
Not on the map. Why?
Abstract (arXiv)
Global sensitivity analysis (GSA) aims to detect influential input factors that lead a model to arrive at a certain decision and is a significant approach for mitigating the computational burden of processing high dimensional data. In this paper, we provide a comprehensive review and a comparison on global sensitivity analysis methods. Additionally, we propose a methodology for evaluating the efficacy of these methods by conducting a case study on MNIST digit dataset. Our study goes through the underlying mechanism of widely used GSA methods and highlights their efficacy through a comprehensive methodology.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 41 references that are in the map. First posted on arXiv 23 Jun 2024.
Builds on
9 of 41 references.
References · 9
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
- 2017Learning Important Features Through Propagating Activation Differencescited by 1,458
- 2017Visualizing the Loss Landscape of Neural Netscited by 1,130
- 2017Interpreting Deep Visual Representations via Network Dissectioncited by 103
- 2019Explaining Models by Propagating Shapley Values of Local Componentscited by 29
- 2024Causal Generative Explainers using Counterfactual Inference: A Case Study on the Morpho-MNIST Datasetcited by 4
- 2023Evolutionary Augmentation Policy Optimization for Self-supervised Learningcited by 2
Built on by
Citing papers per year: 2025: 1.
1 paper.