Discriminative training lets a few labeled examples steer generative models to target regions
caglar_ee · x · 2026-11-08
Gábor Sár (caglaree) explains that classical unsupervised fitting of probabilistic generative models aims to explain all unlabeled data without distinguishing regions of interest from irrelevant ones. Incorporating discriminative training, however, lets a relatively small number of labeled examples guide the generative model toward covering certain regions of the unlabeled data while avoiding others.
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