Summary of Integrative Cam: Adaptive Layer Fusion For Comprehensive Interpretation Of Cnns, by Aniket K. Singh et al.
Integrative CAM: Adaptive Layer Fusion for Comprehensive Interpretation of CNNs
by Aniket K. Singh, Debasis Chaudhuri, Manish P. Singh, Samiran Chattopadhyay
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Integrative CAM, an advanced Class Activation Mapping (CAM) technique that provides a holistic view of feature importance across Convolutional Neural Networks (CNNs). Traditional gradient-based CAM methods often neglect critical features derived from intermediate layers. Integrative CAM fuses insights across all network layers, leveraging both gradient and activation scores to adaptively weight layer contributions, yielding a comprehensive interpretation of the model’s internal representation. The approach includes a novel bias term in the saliency map calculation, expanding CAM applicability across a wider range of models. Through extensive experiments on diverse datasets, Integrative CAM demonstrates superior fidelity in feature importance mapping, enhancing interpretability for intricate fusion scenarios and complex decision-making tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Integrative CAM is a new way to understand how deep learning models work. It helps explain what features are important in Convolutional Neural Networks (CNNs). Traditional methods only look at the final layer of the model, but this one looks at all layers. It uses two types of information: how much each feature affects the output and how strongly it’s activated. This makes the results more accurate and helpful for complex tasks. The paper shows that Integrative CAM works well on different datasets and can help make deep learning models more trustworthy. |
Keywords
» Artificial intelligence » Deep learning
