"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers"

Multi-Head Explainer (MHEX) enhances both explainability and accuracy in neural networks by dynamically highlighting task-relevant features while maintaining model performance.

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https://arxiv.org/abs/2501.01311

🤔 Original Problem:

→ Current explainability methods like GradCAM and SHAP struggle to capture fine details in medical images and often produce misleading interpretations

→ Transformer models face over-smoothing issues where attention mechanisms generate uniform distributions, diluting interpretative power

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🔧 Solution in this Paper:

→ MHEX introduces three core components that work together to enhance model interpretability

→ An Attention Gate dynamically weighs important features using both local and global information

→ Deep Supervision guides early layers to capture fine-grained details specific to target classes

→ An Equivalent Matrix combines refined local and global representations to generate comprehensive saliency maps

→ The framework integrates seamlessly into existing CNN and Transformer architectures with minimal modifications

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💡 Key Insights:

→ Non-negativity constraints reduce noise in saliency maps

→ Early layer supervision improves feature capture

→ Collaboration between components enhances overall interpretability

→ Framework is modular and adaptable across architectures

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📊 Results:

→ ImageNet1k: 70.57% vs 69.75% baseline accuracy

→ PathMNIST: 95.18% vs 90.90% baseline accuracy

→ OrganAMNIST: 97.66% vs 95.10% baseline accuracy

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