Methods › General › Activation Functions › HardELiSH
HardELiSH
Introduced by Mina Basirat et al. in The Quest for the Golden Activation Function
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
HardELiSH is an activation function for neural networks. The HardELiSH is a multiplication of the HardSigmoid and ELU in the negative part and a multiplication of the Linear and the HardSigmoid in the positive part:
f(x) = xmax(0, min(1, ((x+1)/2)) ) if x ≥1 f(x) = (eˣ-1)max(0, min(1, ((x+1)/2))) if x < 0
Source: Activation Functions
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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The Quest for the Golden Activation Function 2 Aug 2018 · 0 repositories · arXiv:1808.00783
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Image Classification | 1 |
| image-classification | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections