@article{MB1CF4D04,
title = "Clustering Performance Analysis of Autoencoder with Skip Connection",
journal = "The Transactions of the Korea Information Processing Society",
year = "2020",
issn = "null",
doi = "https://doi.org/10.3745/KTSDE.2020.9.12.403",
author = "In-su Jo/Yunhee Kang/Dong-bin Choi/Young B. Park",
keywords = "Skip Connection, Autoencoder, Clustering, Superresolution",
abstract = "In addition to the research on noise removal and super-resolution using the data restoration (Output result) function of Autoencoder, research on the performance improvement of clustering using the dimension reduction function of autoencoder are actively being conducted. The clustering function and data restoration function using Autoencoder have common points that both improve performance through the same learning. Based on these characteristics, this study conducted an experiment to see if the autoencoder model designed to have excellent data recovery performance is superior in clustering performance. Skip connection technique was used to design autoencoder with excellent data recovery performance. The output result performance and clustering performance of both autoencoder model with Skip connection and model without Skip connection were shown as graph and visual extract. The output result performance was increased, but the clustering performance was decreased. This result indicates that the neural network models such as autoencoders are not sure that each layer has learned the characteristics of the data well if the output result is good. Lastly, the performance degradation of clustering was compensated by using both latent code and skip connection. This study is a prior study to solve the Hanja Unicode problem by clustering."
}