HardELiSH

2000년 도입 · 논문 1편에서 사용

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: $$f\left(x\right) = x\max\left(0, \min\left(1, \left(\frac{x+1}{2}\right)\right) \right) \text{ if } x \geq 1$$ $$f\left(x\right) = \left(e^{x}-1\right)\max\left(0, \min\left(1, \left(\frac{x+1}{2}\right)\right)\right) \text{ if } x < 0 $$ Source: Activation Functions

출처: The Quest for the Golden Activation Function

소개 논문: The Quest for the Golden Activation Function