0% Complete
Home
/
11th International Conference on Computer and Knowledge Engineering
PowerLinear Activation Functions with application to the first layer of CNNs
Authors :
Kamyar Nasiri
1
Kamaledin Ghiasi-Shirazi
2
1- Computer Engineering Dept. Ferdowsi University of Mashhad Mashhad, Iran
2- Computer Engineering Dept. Ferdowsi University of Mashhad Mashhad, Iran
Keywords :
PowerLinear activation function, Activation function, Kernel methods, Generalized convolution operators, CNNs
Abstract :
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.
Papers List
List of archived papers
Speech Emotion Recognition Using a Hierarchical Adaptive Weighted Multi-Layer Sparse Auto-Encoder Extreme Learning Machine with New Weighting and Spectral/SpectroTemporal Gabor Filter Bank Features
Fatemeh Daneshfar - Seyed Jahanshah Kabudian
No-Reference Video Quality Assessment by Deep Feature Maps Relations
Amir Hossein Bakhtiari - Azadeh Mansouri
An Efficient Approach for Breast Abnormality Detection through High-Level Features of Thermography Images
Farhad Abedinzadeh Torghabeh - Yeganeh Modaresnia - Seyyed Abed Hosseini
Graph Representation Learning Towards Patents Network Analysis
Mohammad Heydari - Babak Teimourpour
Adaptive Active Queue Management for Time Slot Channel Hopping in Industrial Internet of Things
Mehdi Zirak - Yasser Sedaghat - Mohammad Hossein Yaghmaee Moghaddam
Explainable Error Detection Method for Structured Data using HoloDetect framework
Abolfazl Mohajeri Khorasani - Sahar Ghassabi - Behshid Behkamal - Mostafa Milani
African Vultures Optimization Algorithm for Optimal Damping Controllers Design in the Electrical Power Grid System
Aliyu Sabo - Theophilus Ebuka Odoh - Samuel Habu - Hossein Shahinzadeh - Farshad Ebrahimi
A scalable blockchain-based educational network for data storage and assessment
Maryam Fattahi Vanani - Hamidreza Shayegh Borujeni - Ali Nourollah
Evolutionary Approach to GAN Hyperparameter Tuning: Minimizing Discriminator and Generator Loss Functions
Sajad Haghzad Klidbary - Anahita Babaei - Ramin Ghorbani
Automating Theory of Mind Assessment with a LLaMA-3-Powered Chatbot: Enhancing Faux Pas Detection in Autism
Avisa Fallah - Ali Keramati - Mohammad Ali Nazari - Fatemeh Sadat Mirfazeli
more
Samin Hamayesh - Version 41.7.6