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CF-CAM: Cluster Filter Class Activation Mapping for Reliable Gradient-Based Interpretability

Hongjie He, Xu Pan, Yudong Yao

TL;DR

The paper tackles the challenge of trustworthy interpretability in deep CNNs by addressing gradient-noise and efficiency concerns in visual explanations. It introduces CF-CAM, a two-stage framework that combines density-aware channel clustering (via DBSCAN) with cluster-conditioned gradient filtering and a hierarchical weighting scheme to produce faithful and edge-aware heatmaps. Experimental validation on the Shenzhen Hospital X-ray Set shows that CF-CAM achieves superior faithfulness and robustness compared with state-of-the-art CAM methods, while maintaining practical inference times. The work demonstrates CF-CAM’s potential for high-stakes applications such as medical diagnosis and autonomous driving, and suggests avenues for extending the approach to multi-modal models and broader architectures.

Abstract

As deep learning continues to advance, the transparency of neural network decision-making remains a critical challenge, limiting trust and applicability in high-stakes domains. Class Activation Mapping (CAM) techniques have emerged as a key approach toward visualizing model decisions, yet existing methods face inherent trade-offs. Gradient-based CAM variants suffer from sensitivity to gradient perturbations due to gradient noise, leading to unstable and unreliable explanations. Conversely, gradient-free approaches mitigate gradient instability but incur significant computational overhead and inference latency. To address these limitations, we propose a Cluster Filter Class Activation Map (CF-CAM) technique, a novel framework that reintroduces gradient-based weighting while enhancing robustness against gradient noise. CF-CAM utilizes hierarchical importance weighting strategy to balance discriminative feature preservation and noise elimination. A density-aware channel clustering method via Density-Based Spatial Clustering of Applications with Noise (DBSCAN) groups semantically relevant feature channels and discard noise-prone activations. Additionally, cluster-conditioned gradient filtering leverages Gaussian filters to refine gradient signals, preserving edge-aware localization while suppressing noise impact. Experiment results demonstrate that CF-CAM achieves superior interpretability performance while enhancing computational efficiency, outperforming state-of-the-art CAM methods in faithfulness and robustness. By effectively mitigating gradient instability without excessive computational cost, CF-CAM provides a competitive solution for enhancing the interpretability of deep neural networks in critical applications such as autonomous driving and medical diagnosis.

CF-CAM: Cluster Filter Class Activation Mapping for Reliable Gradient-Based Interpretability

TL;DR

The paper tackles the challenge of trustworthy interpretability in deep CNNs by addressing gradient-noise and efficiency concerns in visual explanations. It introduces CF-CAM, a two-stage framework that combines density-aware channel clustering (via DBSCAN) with cluster-conditioned gradient filtering and a hierarchical weighting scheme to produce faithful and edge-aware heatmaps. Experimental validation on the Shenzhen Hospital X-ray Set shows that CF-CAM achieves superior faithfulness and robustness compared with state-of-the-art CAM methods, while maintaining practical inference times. The work demonstrates CF-CAM’s potential for high-stakes applications such as medical diagnosis and autonomous driving, and suggests avenues for extending the approach to multi-modal models and broader architectures.

Abstract

As deep learning continues to advance, the transparency of neural network decision-making remains a critical challenge, limiting trust and applicability in high-stakes domains. Class Activation Mapping (CAM) techniques have emerged as a key approach toward visualizing model decisions, yet existing methods face inherent trade-offs. Gradient-based CAM variants suffer from sensitivity to gradient perturbations due to gradient noise, leading to unstable and unreliable explanations. Conversely, gradient-free approaches mitigate gradient instability but incur significant computational overhead and inference latency. To address these limitations, we propose a Cluster Filter Class Activation Map (CF-CAM) technique, a novel framework that reintroduces gradient-based weighting while enhancing robustness against gradient noise. CF-CAM utilizes hierarchical importance weighting strategy to balance discriminative feature preservation and noise elimination. A density-aware channel clustering method via Density-Based Spatial Clustering of Applications with Noise (DBSCAN) groups semantically relevant feature channels and discard noise-prone activations. Additionally, cluster-conditioned gradient filtering leverages Gaussian filters to refine gradient signals, preserving edge-aware localization while suppressing noise impact. Experiment results demonstrate that CF-CAM achieves superior interpretability performance while enhancing computational efficiency, outperforming state-of-the-art CAM methods in faithfulness and robustness. By effectively mitigating gradient instability without excessive computational cost, CF-CAM provides a competitive solution for enhancing the interpretability of deep neural networks in critical applications such as autonomous driving and medical diagnosis.

Paper Structure

This paper contains 25 sections, 8 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Density-Aware Channel Clustering.
  3. Cluster-Conditioned Gradient Filtering.
  4. Hierarchical Importance Weighting.
  5. Related Work
  6. CAM Interpretability Methods
  7. Gradient-Based CAM Methods
  8. Gradient-Free CAM Methods
  9. Non-CAM Interpretability Methods
  10. Methodology
  11. CF-CAM Architecture
  12. Channel Clustering Stage
  13. Gradient Filtering Stage
  14. Addresing Gradient Issues
  15. Experiments
  16. ...and 10 more sections

Figures (5)

  • Figure 1: Overall pipeline of proposed CF-CAM.
  • Figure 2: Visualization results of feature maps obtained from Channel Clustering Stage.
  • Figure 3: CAMs generated by different methods for four clinical cases from the Shenzhen Hospital X-ray dataset.
  • Figure 4: Average SSIM and MSE vs. Noise Level for Different CAM Methods based on gradient.
  • Figure 5: Original and Noisy heatmaps of Gradient-Based CAM Methods on a image randomly chosen from the Shenzhen Hospital X-ray Set. ($\sigma=5$)