mlsanityData to 14 Oct 2026

Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method

arXiv 2101.10710 ↗First posted 26 Jan 2021cs.CV cs.AI cs.HCcited by 10

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Abstract (arXiv)

Explainable Artificial Intelligence (XAI) has in recent years become a well-suited framework to generate human understandable explanations of "black-box" models. In this paper, a novel XAI visual explanation algorithm known as the Similarity Difference and Uniqueness (SIDU) method that can effectively localize entire object regions responsible for prediction is presented in full detail. The SIDU algorithm robustness and effectiveness is analyzed through various computational and human subject experiments. In particular, the SIDU algorithm is assessed using three different types of evaluations (Application, Human and Functionally-Grounded) to demonstrate its superior performance. The robustness of SIDU is further studied in the presence of adversarial attack on "black-box" models to better understand its performance. Our code is available at: https://github.com/satyamahesh84/SIDU_XAI_CODE.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 19 of its 41 references that are in the map. First posted on arXiv 26 Jan 2021.

Builds on

19 of 41 references.

References · 19

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  12. 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 838
7 more
  1. 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 612
  2. 2016What do different evaluation metrics tell us about saliency models?cited by 185
  3. 2016Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?cited by 111
  4. 2020Towards Robust Explanations for Deep Neural Networkscited by 43
  5. 2019Iterative Augmentation of Visual Evidence for Weakly-Supervised Lesion Localization in Deep Interpretability Frameworks: Application to Color Fundus Imagescited by 6
  6. 2020SIDU: Similarity Difference and Uniqueness Method for Explainable AIcited by 4
  7. 2018Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detectioncited by 1

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 5; 2024: 4.

10 papers.

  1. 2023Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations Datasetscited by 15
  2. 2023Human Attention-Guided Explainable Artificial Intelligence for Computer Vision Modelscited by 6
  3. 2021Believe The HiPe: Hierarchical Perturbation for Fast, Robust, and Model-Agnostic Saliency Mappingcited by 5
  4. 2024Verifying Machine Unlearning with Explainable AIcited by 5
  5. 2024SIDU-TXT: An XAI Algorithm for NLP with a Holistic Assessment Approachcited by 4
  6. 2023PAMI: partition input and aggregate outputs for model interpretationcited by 2
  7. 2023Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligencecited by 2
  8. 2023QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systemscited by 1
  9. 2024Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Modelscited by 1
  10. 2024Integrative CAM: Adaptive Layer Fusion for Comprehensive Interpretation of CNNscited by 0