NiaNet: A framework for constructing Autoencoder architectures using nature-inspired algorithms
Sašo Pavlič, Iztok Fister, Sašo Karakatič · Annals of Computer Science and Information Systems · 2022
Autoencoder, an hourly glass-shaped deep neural network capable of learning data representation in a lower dimension, has performed well in various applications.However, developing a high-quality AE system for a specific task heavily relies on human expertise, limiting its widespread application.On the other hand, there has been a gradual increase in automated machine learning for developing deep learning systems without human intervention.However, there is a shortage of automatically designing particular deep neural networks such as AE.This study presents the NiaNet method and corresponding software framework for designing AE topology and hyper-parameter settings.Our findings show that it is possible to discover the optimal AE architecture for a specific dataset without the requirement for human expert assistance.The future potential of the proposed method is also discussed in this paper.