Methods › General › Adaptive Activation Functions › ReLUN
Rectified Linear Unit N
ReLUN
Introduced by Evgenii Pishchik in Trainable Activations for Image Classification
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The Rectified Linear Unit N, or ReLUN, is a modification of ReLU6 activation function that has trainable parameter n.
ReLUN(x) = min(max(0, x), n)
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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Trainable Activations for Image Classification 26 Jan 2023 · 1 repository
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
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