Parametric ReLU (PReLU)
Definition
PReLU learns the response of an activation function to negative inputs.
A ReLU variant whose slope for negative inputs is learned.
Simply explained
A computational function learns by itself how strongly negative input values should propagate.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A CNN learns a parameter instead of a fixed negative Leaky ReLU slope.
Limits and pitfalls
- Automatically assuming more parameters mean better generalization or ignoring initialization.
Origin & history
He and co-authors introduced PReLU in 2015 in the context of deep image networks.
Use in mid-sized companies
- Possible example: A CNN learns a parameter instead of a fixed negative Leaky ReLU slope.
Frequently asked questions
What does Parametric ReLU mean?
A ReLU variant whose slope for negative inputs is learned. What mistakes should be avoided? Automatically assuming more parameters mean better generalization or ignoring initialization.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: