no code implementations • 1 Jan 2021 • Benoit Gaujac, Ilya Feige, David Barber
We further study the trade off between disentanglement and reconstruction on more-difficult data sets with unknown generative factors, where we expect improved reconstructions due to the flexibility of the WAE paradigm.
no code implementations • 7 Oct 2020 • Benoit Gaujac, Ilya Feige, David Barber
Probabilistic models with hierarchical-latent-variable structures provide state-of-the-art results amongst non-autoregressive, unsupervised density-based models.
no code implementations • 7 Oct 2020 • Benoit Gaujac, Ilya Feige, David Barber
We further study the trade off between disentanglement and reconstruction on more-difficult data sets with unknown generative factors, where the flexibility of the WAE paradigm in the reconstruction term improves reconstructions.
no code implementations • 12 Jun 2018 • Benoit Gaujac, Ilya Feige, David Barber
Generative models with both discrete and continuous latent variables are highly motivated by the structure of many real-world data sets.