gradcam

**Grad-CAM** (Gradient-weighted Class Activation Mapping) is a **visual explanation technique that produces a coarse localization map highlighting the important regions in an image** — using the gradients flowing into the last convolutional layer to weight the activation maps by their importance for the target class. **How Grad-CAM Works** - **Gradients**: Compute gradients of the target class score with respect to feature maps of the last conv layer. - **Weights**: Global average pool the gradients to get importance weights $alpha_k$ for each feature map $k$. - **CAM**: $L_{Grad-CAM} = ReLU(sum_k alpha_k A_k)$ — weighted sum of feature maps, ReLU keeps only positive influence. - **Upsampling**: Upsample the CAM to input image resolution for overlay visualization. **Why It Matters** - **Model-Agnostic**: Works with any CNN architecture that has convolutional layers. - **Class-Discriminative**: Different target classes produce different heat maps — shows what the model looks for per class. - **No Retraining**: Post-hoc technique — no modification to the model architecture or training. **Grad-CAM** is **seeing what the CNN sees** — highlighting the image regions that most influenced the classification decision.