Guided Backpropagation is a visualization technique that modifies the standard backpropagation to produce sharper, more interpretable saliency maps — by additionally masking out negative gradients at ReLU layers during the backward pass, keeping only features that both activated the neuron and had positive gradient.

How Guided Backpropagation Works

• Standard Backprop: Passes gradients through ReLU if the input was positive (forward mask).
• Deconvolution: Passes gradients through ReLU if the gradient is positive (backward mask).
• Guided Backprop: Applies BOTH masks — gradient passes only if both input AND gradient are positive.
• Result: Highlights fine-grained input features that positively contribute to the activation of higher layers.

Why It Matters

• Sharp Maps: Produces much sharper, more visually detailed saliency maps than vanilla gradients.
• Feature-Level: Shows individual edges, textures, and patterns rather than blurry activation regions.
• Limitation: Not class-discriminative — guided Grad-CAM combines it with Grad-CAM for class-specific, high-resolution maps.

Guided Backpropagation is the double-filtered gradient — keeping only the positive signals in both forward and backward passes for crisp saliency maps.