Deep clustering with fusion autoencoder. (arXiv:2201.04727v1 [cs.LG])

Embracing the deep learning techniques for representation learning in
clustering research has attracted broad attention in recent years, yielding a
newly developed clustering paradigm, viz. the deep clustering (DC). Typically,
the DC models capitalize on autoencoders to learn the intrinsic features which
facilitate the clustering process in consequence. Nowadays, a generative model
named variational autoencoder (VAE) has got wide acceptance in DC studies.
Nevertheless, the plain VAE is insufficient to perceive the comprehensive
latent features, leading to the deteriorative clustering …

arxiv autoencoder clustering