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name: saliency-and-cam description: "Routes users who need saliency, guided or rectified saliency, Grad-CAM, custom-loss saliency, or regression attention workflows for keras-vis." disable-model-invocation: true metadata: disco-role: operating license: MIT

Saliency and CAM

Use this sub-skill when the user wants input-space attention maps from a trained keras-vis model.

Covers

• visualize_saliency
• visualize_saliency_with_losses
• visualize_cam
• visualize_cam_with_losses
• regression attention with grad_modifier
• guided or rectified saliency with backprop_modifier
• penultimate-layer selection for Grad-CAM

Routes out

• Activation image synthesis belongs in ../activation-maximization/SKILL.md.
• New loss/optimizer internals belong in ../optimization-building-blocks/SKILL.md.
• Heatmap overlay, color conversion, and image composition belong in ../image-utilities/SKILL.md.

What to read first

• references/api-reference.md for exact signatures, defaults, shapes, and modifiers.
• references/workflows.md for classification, regression, and Grad-CAM recipes.
• references/troubleshooting.md for penultimate-layer, modifier, and shape recovery.

Smoke check

Use scripts/saliency_smoke.py to build a tiny deterministic model and print saliency and optional CAM heatmap shapes and value ranges.

Practical rule

Prefer saliency when the model has no close convolutional or pooling layer before the target. Use CAM when a nearby spatial layer exists and you want a class-localized spatial explanation.