A statistical theory of semi-supervised learning
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We currently lack a solid statistical understanding of semi-supervised learning methods, instead treating them as a collection of highly effective tricks. This precludes the principled combination e.g. of Bayesian methods and semi-supervised learning, as semi-supervised learning objectives are not currently formulated as likelihoods for an underlying generative model of the data. Here, we note that standard image benchmark datasets such as CIFAR-10 are carefully curated, and we provide a generative model describing the curation process. Under this generative model, several state-of-the-art semi-supervised learning techniques, including entropy minimization, pseudo-labelling and the FixMatch family emerge naturally as variational lower-bounds on the log-likelihood.
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