Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System
DOI:
https://doi.org/10.21609/jiki.v13i1.761Keywords:
variational autoencoder, feature extraction, deep learning, computer vision, fire detection systemAbstract
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.
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