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Variational Information Bottleneck for Semi-Supervised Classification
2020
Entropy
In this paper, we consider an information bottleneck (IB) framework for semi-supervised classification with several families of priors on latent space representation. We apply a variational decomposition of mutual information terms of IB. Using this decomposition we perform an analysis of several regularizers and practically demonstrate an impact of different components of variational model on the classification accuracy. We propose a new formulation of semi-supervised IB with hand crafted and
doi:10.3390/e22090943
pmid:33286710
pmcid:PMC7597214
fatcat:chlv5b45qreknpghsaguyt2dce