Semi-Supervised Learning with Deep Generative Models

The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inflexible, inefficient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Classification SVHN M1+M2 Percentage error 36.02 # 53
Image Classification SVHN DGN Percentage error 36.02 # 53
Image Classification SVHN M1+KNN Percentage error 65.63 # 56
Image Classification SVHN M1+TSVM Percentage error 54.33 # 55


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