Deep clustering with fusion autoencoder

TL;DR

This paper introduces a fusion autoencoder combining variational autoencoders and generative adversarial networks, enhanced with residual networks, to improve deep clustering performance on image datasets.

Contribution

The paper proposes a novel fusion autoencoder that integrates VAE and GAN with residual architecture for better feature learning in deep clustering.

Findings

Outperforms baseline methods on multiple image datasets

Enhances discriminative feature learning for clustering

Improves cluster separation and data point cohesion

Abstract

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.