  In this paper, we introduce "Power Linear Unit" (PoLU) which increases the
nonlinearity capacity of a neural network and thus helps improving its
performance. PoLU adopts several advantages of previously proposed activation
functions. First, the output of PoLU for positive inputs is designed to be
identity to avoid the gradient vanishing problem. Second, PoLU has a non-zero
output for negative inputs such that the output mean of the units is close to
zero, hence reducing the bias shift effect. Thirdly, there is a saturation on
the negative part of PoLU, which makes it more noise-robust for negative
inputs. Furthermore, we prove that PoLU is able to map more portions of every
layer's input to the same space by using the power function and thus increases
the number of response regions of the neural network. We use image
classification for comparing our proposed activation function with others. In
the experiments, MNIST, CIFAR-10, CIFAR-100, Street View House Numbers (SVHN)
and ImageNet are used as benchmark datasets. The neural networks we implemented
include widely-used ELU-Network, ResNet-50, and VGG16, plus a couple of shallow
networks. Experimental results show that our proposed activation function
outperforms other state-of-the-art models with most networks.
