CGMVAE: Coupling GMM Prior and GMM Estimator for Unsupervised Clustering and Disentanglement

Abstract

Impressive progress has been recently witnessed on deep unsupervised clustering and feature disentanglement. In this paper, we propose a novel method on top of one recent architecture with a novel explanation of Gaussian mixture model (GMM) membership, accompanied by a GMM loss to enhance the clustering. The GMM loss is optimized with the explicitly computed parameters under our coupled GMM inspired framework. Specifically, our model takes the advantage of implicitly learning a GMM in latent space by neural networks (GMM prior as the first GMM), and explicitly clustering via the other GMM framework (GMM estimator as the second GMM). We further introduce a Dirichlet conjugate loss as a regularization term to prevent the GMM estimator from degenerating to few Gaussians. Eventually, we further propose an application of apparel generation based on the proposed method which requires only three selection steps. Extensive experiments on publicly available datasets demonstrate the effectiveness of our method, in terms of clustering and disentanglement performance.