Variational Information Bottleneck for Semi-Supervised Classification

Slava Voloshynovskiy, Olga Taran, Mouad Kondah, Taras Holotyak, Danilo Rezende
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
more » ... earnable priors and link it to the previous methods such as semi-supervised versions of VAE (M1 + M2), AAE, CatGAN, etc. We show that the resulting model allows better understand the role of various previously proposed regularizers in semi-supervised classification task in the light of IB framework. The proposed IB semi-supervised model with hand-crafted and learnable priors is experimentally validated on MNIST under different amount of labeled data.
doi:10.3390/e22090943 pmid:33286710 pmcid:PMC7597214 fatcat:chlv5b45qreknpghsaguyt2dce