655 citations · 2.7k across the 49 of their papers we have counts for
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cs.LG2019★ 3 cited
LAVAE: Disentangling Location and Appearance
Andrea Dittadi, Ole Winther
We propose a probabilistic generative model for unsupervised learning of structured, interpretable, object-based representations of visual scenes. We use amortized variational infe…
stat.ML2019★ 116 cited
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
Lars Maaløe, Marco Fraccaro, Valentin Liévin +1
With the introduction of the variational autoencoder (VAE), probabilistic latent variable models have received renewed attention as powerful generative models. However, their perfo…