LeakyReLU is a variation of the ReLU activation function:
$$ f(x) = \max(\alpha x, x) $$
It leaves positive inputs unvaried while multiplying the negative ones by a small positive constant $\alpha$. Unlike ReLU, which outputs zero for any negative input, Leaky ReLU keeps a small slope on the negative side, allowing gradients to flow through neurons with negative inputs during backpropagation, and mitigating the dying ReLU problem.
In standard Leaky ReLU, $\alpha$ is a fixed hyperparameter, while in [PReLU] it is learned during training.