SiLU / Swish
Definition
SiLU and Swish are smooth activation functions for processing values in neural networks.
A smooth, non-monotonic activation function f(x)=x·sigmoid(x). This function is called SiLU and corresponds to Swish with fixed parameter β=1; the general Swish family can use other β values.
Simply explained
Like a continuously variable controller, they change how strongly a number is passed on to the next processing layer.
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 neural network replaces ReLU with SiLU in its hidden layers.
Limits and pitfalls
- Equating SiLU with every parameterized Swish variant, interpreting the name as a different function, or claiming general superiority over ReLU or GELU.
Origin & history
Elfwing and co-authors described SiLU in 2017; Ramachandran and co-authors also studied and popularized Swish in 2017.
Use in mid-sized companies
- Possible application: A neural network replaces ReLU with SiLU in its hidden layers.
Frequently asked questions
What does SiLU / Swish mean?
A smooth, non-monotonic activation function f(x)=x·sigmoid(x). This function is called SiLU and corresponds to Swish with fixed parameter β=1; the general Swish family can use other β values. What mistakes should be avoided? Equating SiLU with every parameterized Swish variant, interpreting the name as a different function, or claiming general superiority over ReLU or GELU.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no proof of real customer results.
Editorially reviewed on: