Activation Functions
HardELiSH
Introduced by Basirat et al. in The Quest for the Golden Activation FunctionHardELiSH 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:
$$f\left(x\right) = x\max\left(0, \min\left(1, \left(\frac{x+1}{2}\right)\right) \right) \text{ if } x \geq 1$$ $$f\left(x\right) = \left(e^{x}-1\right)\max\left(0, \min\left(1, \left(\frac{x+1}{2}\right)\right)\right) \text{ if } x < 0 $$
Source: Activation Functions
Source: The Quest for the Golden Activation Function
Papers
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
