# Integrative CAM: Adaptive Layer Fusion for Comprehensive Interpretation of CNNs

arXiv: https://arxiv.org/abs/2412.01354 · HTML: https://mlsanity.com/paper/2412.01354/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 19 of 19)

- [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)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (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)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Concept Whitening for Interpretable Image Recognition](https://mlsanity.com/paper/2002.01650/) (2020)
- [Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning](https://mlsanity.com/paper/1912.08881/) (2019)
- [Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications](https://mlsanity.com/paper/2103.04244/) (2021)
- [Towards Robust Explanations for Deep Neural Networks](https://mlsanity.com/paper/2012.10425/) (2020)
- [Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method](https://mlsanity.com/paper/2101.10710/) (2021)
- [Feature Perturbation Augmentation for Reliable Evaluation of Importance Estimators in Neural Networks](https://mlsanity.com/paper/2303.01538/) (2023)

## Abstract (arXiv)

With the growing demand for interpretable deep learning models, this paper introduces Integrative CAM, an advanced Class Activation Mapping (CAM) technique aimed at providing a holistic view of feature importance across Convolutional Neural Networks (CNNs). Traditional gradient-based CAM methods, such as Grad-CAM and Grad-CAM++, primarily use final layer activations to highlight regions of interest, often neglecting critical features derived from intermediate layers. Integrative CAM addresses this limitation by fusing insights across all network layers, leveraging both gradient and activation scores to adaptively weight layer contributions, thus yielding a comprehensive interpretation of the model's internal representation. Our approach includes a novel bias term in the saliency map calculation, a factor frequently omitted in existing CAM techniques, but essential for capturing a more complete feature importance landscape, as modern CNNs rely on both weighted activations and biases to make predictions. Additionally, we generalize the alpha term from Grad-CAM++ to apply to any smooth function, expanding CAM applicability across a wider range of models. Through extensive experiments on diverse and complex datasets, Integrative CAM demonstrates superior fidelity in feature importance mapping, effectively enhancing interpretability for intricate fusion scenarios and complex decision-making tasks. By advancing interpretability methods to capture multi-layered model insights, Integrative CAM provides a valuable tool for fusion-driven applications, promoting the trustworthy and insightful deployment of deep learning models.