DeepLIFT (Deep Learning Important FeaTures) is an attribution method that explains predictions by comparing neuron activations to their reference activations — decomposing the difference between the output and a reference output into contributions from each input feature.

How DeepLIFT Works

• Reference: A reference input $x_0$ (analogous to Integrated Gradients' baseline) with known activations.
• Difference: For each neuron, compute the difference from reference: $Delta y = y - y_0$.
• Contribution Rule: Assign contributions $C(Delta x_i)$ to each input such that $sum_i C(Delta x_i) = Delta y$.
• Rules: Rescale rule (proportional to activation difference) or RevealCancel rule (separates positive and negative contributions).

Why It Matters

• Summation Property: Contributions from all features sum exactly to the prediction difference — complete attribution.
• Beyond Gradients: DeepLIFT handles saturated activations better than raw gradients (which are zero at saturation).
• Efficiency: Requires only one forward + one backward pass (no iterative interpolation like Integrated Gradients).

DeepLIFT is attribution by comparison — explaining how much each feature contributes to the prediction relative to a reference baseline.