Ablation-CAM is a class activation mapping variant that determines feature map importance by ablation — systematically removing (zeroing out) each feature map and measuring the drop in the target class score, providing a principled, gradient-free importance measure.

How Ablation-CAM Works

• Baseline: Record the target class score with all feature maps present.
• Ablation: For each feature map $A_k$, zero it out and re-forward — record the score drop $Delta s_k$.
• Weights: The importance weight for map $k$ is proportional to the score drop when $A_k$ is removed.
• CAM: $L_{Ablation} = ReLU(sum_k Delta s_k cdot A_k)$ — weight maps by their ablation importance.

Why It Matters

• Causal: Ablation directly measures causal importance — "removing this feature reduced the score by X."
• No Gradients: Like Score-CAM, avoids gradient issues — suitable for non-differentiable models.
• Validation: Can validate Grad-CAM explanations by checking if gradient-based and ablation-based importance agree.

Ablation-CAM is remove-and-measure — determining each feature map's importance by testing what happens when it's removed.