An Efficient Asymmetric Nonlinear Activation Function for Deep Neural Networks

As a key step to endow the neural network with nonlinear factors, the activation function is crucial to the performance of the network. This paper proposes an Efficient Asymmetric Nonlinear Activation Function (EANAF) for deep neural networks. Compared with existing activation functions, the proposed EANAF requires less computational effort, and it is self-regularized, asymmetric and non-monotonic. These desired characteristics facilitate the outstanding performance of the proposed EANAF. To demonstrate the effectiveness of this function in the field of object detection, the proposed activation function is compared with several state-of-the-art activation functions on the typical backbone networks such as ResNet and DSPDarkNet. The experimental results demonstrate the superior performance of the proposed EANAF.

Automated summary

The activation function is crucial to neural network performance. This paper proposes an Efficient Asymmetric Nonlinear Activation Function (EANAF) that requires less computational effort and has self-regularization, asymmetry, and non-monotonicity. Experimental results show the superior performance of EANAF in object detection.