  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 performance. In this
paper, a novel DC method is proposed to address this issue. Specifically, the
generative adversarial network and VAE are coalesced into a new autoencoder
called fusion autoencoder (FAE) for discerning more discriminative
representation that benefits the downstream clustering task. Besides, the FAE
is implemented with the deep residual network architecture which further
enhances the representation learning ability. Finally, the latent space of the
FAE is transformed to an embedding space shaped by a deep dense neural network
for pulling away different clusters from each other and collapsing data points
within individual clusters. Experiment conducted on several image datasets
demonstrate the effectiveness of the proposed DC model against the baseline
methods.
