2021/11/29 by Boxi Duan, Duan, Boxi
Computer Science · Engineering · #Advanced Memory and Neural Computing #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Neural Networks and Applications #Neural Networks and Reservoir Computing #cs.CV
paper · pdf · doi:10.48550/arxiv.2111.14349
arxiv created 2021/11/29 · openalex publication_date 2021/11/29 · arxiv updated 2021/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper proposes a novel and insightful activation method termed FPLUS, which exploits mathematical power function with polar signs in form. It is enlightened by common inverse operation while endowed with an intuitive meaning of bionics. The formulation is derived theoretically under conditions of some prior knowledge and anticipative properties, and then its feasibility is verified through a series of experiments using typical benchmark datasets, whose results indicate our approach owns superior competitiveness among numerous activation functions, as well as compatible stability across many CNN architectures. Furthermore, we extend the function presented to a more generalized type called PFPLUS with two parameters that can be fixed or learnable, so as to augment its expressive capacity, and outcomes of identical tests validate this improvement.