RReLU

2000년 도입 · 논문 3편에서 사용

Randomized Leaky Rectified Linear Units, or RReLU, are an activation function that randomly samples the negative slope for activation values. It was first proposed and used in the Kaggle NDSB Competition. During training, $a\_{ji}$ is a random number sampled from a uniform distribution $U\left(l, u\right)$. Formally: $$ y\_{ji} = x\_{ji} \text{ if } x\_{ji} \geq{0} $$ $$ y\_{ji} = a\_{ji}x\_{ji} \text{ if } x\_{ji} < 0 $$ where $$\alpha\_{ji} \sim U\left(l, u\right), l < u \text{ and } l, u \in \left0,1\right)$$ In the test phase, we take average of all the $a\_{ji}$ in training similar to [dropout, and thus set $a\_{ji}$ to $\frac{l+u}{2}$ to get a deterministic result. As suggested by the NDSB competition winner, $a\_{ji}$ is sampled from $U\left(3, 8\right)$. At test time, we use: $$ y\_{ji} = \frac{x\_{ji}}{\frac{l+u}{2}} $$

출처: Empirical Evaluation of Rectified Activations in Convolutional Network

소개 논문: Empirical Evaluation of Rectified Activations in Convolutional Network