Inverting Variational Autoencoders for Improved Generative Accuracy

arXiv: Learning, 2016.

Cited by: 2|Views52


Recent advances in semi-supervised learning with deep generative models have shown promise in generalizing from small labeled datasets ($mathbf{x},mathbf{y}$) to large unlabeled ones ($mathbf{x}$). In the case where the codomain has known structure, a large unfeatured dataset ($mathbf{y}$) is potentially available. We develop a parameter-...More



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