Cloud-VAE: Variational autoencoder with concepts embedded

Yue Liu, Zitu Liu, Shuang Li, Zhenyao Yu, Yike Guo, Qun Liu, Guoyin Wang

Abstract: Highlights•The initial concepts in latent space are described as prior distribution obtained by the proposed cloud model-based clustering algorithm.•Variational lower bound of Cloud-VAE is derived to guide training process and re-construct concepts of latent space, so that the mutual mapping between latent space and concept space is established.•Reparameterization trick based forward cloud transformation algorithm is designed to constrain the representations range of latent space by increasing the randomness of latent variables.•The experimental results on six benchmark datasets show that Cloud-VAE has good clustering and reconstruction performance. Compared with the deep clustering methods VaDE and GMVAE, Cloud-VAE improved the NMI by 22.9% and 19.9% respectively.•Cloud-VAE can explicitly explain the aggregation process of the model, and other interpretable latent representations are found on top of the existed.