FULLY CONVOLUTIONAL VARIATIONAL AUTOENCODER FOR FEATURE EXTRACTION OF FIRE DETECTION SYSTEM
Dublin Core
Title
FULLY CONVOLUTIONAL VARIATIONAL AUTOENCODER FOR FEATURE EXTRACTION OF FIRE DETECTION SYSTEM
Subject
variational autoencoder, feature extraction, deep learning, computer vision, fire detection system
Description
This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images. The dataset will be used to train the deep learning algorithm to detect fire and smoke. The features extraction is used to tackle the curse of dimensionality, which is the common issue in training deep learning with huge datasets. Features extraction aims to reduce the dimension of the dataset significantly without losing too much essential information. Variational autoencoders (VAEs) are powerfull generative model, which can be used for dimension reduction. VAEs work better than any other methods available for this purpose because they can explore variations on the data in a specific direction
Creator
Herminarto Nugroho, Meredita Susanty, Ade Irawan, Muhammad Koyimatu, Ariana Yunita
Source
: http://dx:doi:org/10:21609/jiki:v13i1:761
Publisher
Faculty of Computer Science Universitas Indonesia
Date
2020-02-28
Contributor
Sri Wahyuni
Rights
e-ISSN : 2502-9274 printed ISSN : 2088-7051
Format
PDF
Language
English
Type
Text
Coverage
Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Files
Collection
Citation
Herminarto Nugroho, Meredita Susanty, Ade Irawan, Muhammad Koyimatu, Ariana Yunita, “FULLY CONVOLUTIONAL VARIATIONAL AUTOENCODER FOR FEATURE EXTRACTION OF FIRE DETECTION SYSTEM,” Repository Horizon University Indonesia, accessed May 22, 2025, https://repository.horizon.ac.id/items/show/8799.