  Convolutional neural networks (CNNs) have become the state-of-the-art tool
for dealing with unsolved problems in computer vision and image processing.
Since the convolution operator is a linear operator, several generalizations
have been proposed to improve the performance of CNNs. One way to increase the
capability of the convolution operator is by applying activation functions on
the inner product operator. In this paper, we will introduce PowerLinear
activation functions, which are based on the polynomial kernel generalization
of the convolution operator. EvenPowLin functions are the main branch of the
PowerLinear activation functions. This class of activation functions is
saturated neither in the positive input region nor in the negative one. Also,
the negative inputs are activated with the same magnitude as the positive
inputs. These features made the EvenPowLin activation functions able to be
utilized in the first layer of CNN architectures and learn complex features of
input images. Additionally, EvenPowLin activation functions are used in CNN
models to classify the inversion of grayscale images as accurately as the
original grayscale images, which is significantly better than commonly used
activation functions.
