# Visual Explanations via Iterated Integrated Attributions

arXiv: https://arxiv.org/abs/2310.18585 · HTML: https://mlsanity.com/paper/2310.18585/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 66 references that are in the map.
- First posted on arXiv 28 Oct 2023.

## Builds on (most cited first, 30 of 47)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [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)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned](https://mlsanity.com/paper/1905.09418/) (2019)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Quantifying Attention Flow in Transformers](https://mlsanity.com/paper/2005.00928/) (2020)
- [Transformer Interpretability Beyond Attention Visualization](https://mlsanity.com/paper/2012.09838/) (2020)
- [Interpretable Explanations of Black Boxes by Meaningful Perturbation](https://mlsanity.com/paper/1704.03296/) (2017)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers](https://mlsanity.com/paper/2103.15679/) (2021)
- [Not Just a Black Box: Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1605.01713/) (2016)

## Built on by (most cited first, 19 of 19)

- [LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity](https://mlsanity.com/paper/2404.03214/) (2024)
- [Token Transformation Matters: Towards Faithful Post-hoc Explanation for Vision Transformer](https://mlsanity.com/paper/2403.14552/) (2024)
- [Where do Large Vision-Language Models Look at when Answering Questions?](https://mlsanity.com/paper/2503.13891/) (2025)
- [Dynamic Accumulated Attention Map for Interpreting Evolution of Decision-Making in Vision Transformer](https://mlsanity.com/paper/2503.14640/) (2025)
- [Comprehensive Information Bottleneck for Unveiling Universal Attribution to Interpret Vision Transformers](https://mlsanity.com/paper/2507.04388/) (2025)
- [LaSM: Layer-wise Scaling Mechanism for Defending Pop-up Attack on GUI Agents](https://mlsanity.com/paper/2507.10610/) (2025)
- [GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models](https://mlsanity.com/paper/2506.18985/) (2025)
- [T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers](https://mlsanity.com/paper/2403.04523/) (2024)
- [DAVE: Distribution-aware Attribution via ViT Gradient Decomposition](https://mlsanity.com/paper/2602.06613/) (2026)
- [Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations](https://mlsanity.com/paper/2511.13081/) (2025)
- [AdaptGrad: Adaptive Sampling to Reduce Noise](https://mlsanity.com/paper/2410.07711/) (2024)
- [Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness](https://mlsanity.com/paper/2603.08309/) (2026)
- [REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability](https://mlsanity.com/paper/2412.08513/) (2024)
- [DEX-AR: A Dynamic Explainability Method for Autoregressive Vision-Language Models](https://mlsanity.com/paper/2603.06302/) (2026)
- [Axiomatization of Gradient Smoothing in Neural Networks](https://mlsanity.com/paper/2407.00371/) (2024)
- [Beyond Output Faithfulness: Learning Attributions that Preserve Computational Pathways](https://mlsanity.com/paper/2509.04588/) (2025)
- [Attribution via Distributional Paths for Information Revelation](https://mlsanity.com/paper/2606.03885/) (2026)
- [Unraveling the Real Working Mechanism and Inherent Flaws of GAE: A Method for Interpreting Transformer Processes from an Economic Perspective](https://mlsanity.com/paper/2609.07213/) (2026)
- [ConEx: Human-Interpretable Saliency Maps via Concept-Aware Attribution](https://mlsanity.com/paper/2610.04605/) (2026)

## Abstract (arXiv)

We introduce Iterated Integrated Attributions (IIA) - a generic method for explaining the predictions of vision models. IIA employs iterative integration across the input image, the internal representations generated by the model, and their gradients, yielding precise and focused explanation maps. We demonstrate the effectiveness of IIA through comprehensive evaluations across various tasks, datasets, and network architectures. Our results showcase that IIA produces accurate explanation maps, outperforming other state-of-the-art explanation techniques.