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Truncated Gaussian-Mixture Variational AutoEncoder
[article]
2021
arXiv
pre-print
Variation Autoencoder (VAE) has become a powerful tool in modeling the non-linear generative process of data from a low-dimensional latent space. Recently, several studies have proposed to use VAE for unsupervised clustering by using mixture models to capture the multi-modal structure of latent representations. This strategy, however, is ineffective when there are outlier data samples whose latent representations are meaningless, yet contaminating the estimation of key major clusters in the
arXiv:1902.03717v3
fatcat:74qmq5dpr5dxrn5eqtqhx2sfdi