score-cam

**Score-CAM** is a **gradient-free class activation mapping method that weights activation maps by their contribution to the model's confidence** — replacing gradient-based weighting with perturbation-based importance, avoiding issues with noisy or vanishing gradients. **How Score-CAM Works** - **Activation Maps**: Extract feature maps from the target convolutional layer. - **Masking**: For each feature map, normalize and use it as a mask on the input image. - **Scoring**: Feed each masked image through the model to get the target class score (the "importance" of that map). - **Combination**: $L_{Score-CAM} = ReLU(sum_k s_k cdot A_k)$ — weight maps by their confidence scores. **Why It Matters** - **No Gradients**: Avoids gradient noise and saturation issues — more stable explanations. - **Faithful**: Importance weights directly measure each map's effect on the model's confidence. - **Trade-Off**: Requires $N$ forward passes (one per activation map) — slower than Grad-CAM but more robust. **Score-CAM** is **measuring importance by masking** — directly testing each feature map's effect on the prediction for gradient-free visual explanations.