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S-shaped ReLU

SReLU

2 papers tagged archive 2025-07-28

Introduced by Xiaojie Jin et al. in Deep Learning with S-shaped Rectified Linear Activation Units

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

The S-shaped Rectified Linear Unit, or SReLU, is an activation function for neural networks. It learns both convex and non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters.

The SReLU is defined as a mapping:

f(x) = tᵢʳ + aʳᵢ(xᵢ-tʳᵢ) if xᵢ ≥tʳᵢ f(x) = xᵢ if tʳᵢ > x > tᵢˡ f(x) = tᵢˡ + aˡᵢ(xᵢ-tˡᵢ) if xᵢ ≤tˡᵢ

where tˡᵢ, tʳᵢ and aˡᵢ are learnable parameters of the network i and indicates that the SReLU can differ in different channels. The parameter aʳᵢ represents the slope of the right line with input above a set threshold. tʳᵢ and tˡᵢ are thresholds in positive and negative directions respectively.

Source: Activation Functions

PaperSourceSee Code · ThomasHagebols/sReLU

Papers archive 2025-07-28

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Tasks archive 2025-07-28

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Deep Learning1

Usage over time archive 2025-07-28

Papers per year tagged with SReLU: 2015 to 2020, peak 1 1 0 2015: 1 paper 2015 2016: 0 papers 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 1 paper 2020
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Categories archive 2025-07-28

Activation Functions

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