The Variational Homoencoder: Learning to learn high capacity generative models from few examples

24 Jul 2018Luke B. HewittMaxwell I. NyeAndreea GaneTommi JaakkolaJoshua B. Tenenbaum

Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot classification, conditional and unconditional generation) as inference within a single generative model. However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typically fail to effectively use latent variables... (read more)

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