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

arXiv: https://arxiv.org/abs/2101.10710 · HTML: https://mlsanity.com/paper/2101.10710/

## 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 (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [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)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Interpretable Explanations of Black Boxes by Meaningful Perturbation](https://mlsanity.com/paper/1704.03296/) (2017)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?](https://mlsanity.com/paper/1606.03556/) (2016)
- [Towards Robust Explanations for Deep Neural Networks](https://mlsanity.com/paper/2012.10425/) (2020)
- [Iterative Augmentation of Visual Evidence for Weakly-Supervised Lesion Localization in Deep Interpretability Frameworks: Application to Color Fundus Images](https://mlsanity.com/paper/1910.07373/) (2019)
- [SIDU: Similarity Difference and Uniqueness Method for Explainable AI](https://mlsanity.com/paper/2006.03122/) (2020)
- [Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection](https://mlsanity.com/paper/1811.11318/) (2018)

## Built on by (most cited first, 10 of 10)

- [Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations Datasets](https://mlsanity.com/paper/2311.01961/) (2023)
- [Human Attention-Guided Explainable Artificial Intelligence for Computer Vision Models](https://mlsanity.com/paper/2305.03601/) (2023)
- [Believe The HiPe: Hierarchical Perturbation for Fast, Robust, and Model-Agnostic Saliency Mapping](https://mlsanity.com/paper/2103.05108/) (2021)
- [Verifying Machine Unlearning with Explainable AI](https://mlsanity.com/paper/2411.13332/) (2024)
- [SIDU-TXT: An XAI Algorithm for NLP with a Holistic Assessment Approach](https://mlsanity.com/paper/2402.03043/) (2024)
- [PAMI: partition input and aggregate outputs for model interpretation](https://mlsanity.com/paper/2302.03318/) (2023)
- [Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence](https://mlsanity.com/paper/2312.01555/) (2023)
- [QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems](https://mlsanity.com/paper/2309.10293/) (2023)
- [Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models](https://mlsanity.com/paper/2410.21129/) (2024)
- [Integrative CAM: Adaptive Layer Fusion for Comprehensive Interpretation of CNNs](https://mlsanity.com/paper/2412.01354/) (2024)

## 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.