285 research outputs found
VAE with a VampPrior
Many different methods to train deep generative models have been introduced
in the past. In this paper, we propose to extend the variational auto-encoder
(VAE) framework with a new type of prior which we call "Variational Mixture of
Posteriors" prior, or VampPrior for short. The VampPrior consists of a mixture
distribution (e.g., a mixture of Gaussians) with components given by
variational posteriors conditioned on learnable pseudo-inputs. We further
extend this prior to a two layer hierarchical model and show that this
architecture with a coupled prior and posterior, learns significantly better
models. The model also avoids the usual local optima issues related to useless
latent dimensions that plague VAEs. We provide empirical studies on six
datasets, namely, static and binary MNIST, OMNIGLOT, Caltech 101 Silhouettes,
Frey Faces and Histopathology patches, and show that applying the hierarchical
VampPrior delivers state-of-the-art results on all datasets in the unsupervised
permutation invariant setting and the best results or comparable to SOTA
methods for the approach with convolutional networks.Comment: 16 pages, final version, AISTATS 201
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
We would like to learn a representation of the data which decomposes an
observation into factors of variation which we can independently control.
Specifically, we want to use minimal supervision to learn a latent
representation that reflects the semantics behind a specific grouping of the
data, where within a group the samples share a common factor of variation. For
example, consider a collection of face images grouped by identity. We wish to
anchor the semantics of the grouping into a relevant and disentangled
representation that we can easily exploit. However, existing deep probabilistic
models often assume that the observations are independent and identically
distributed. We present the Multi-Level Variational Autoencoder (ML-VAE), a new
deep probabilistic model for learning a disentangled representation of a set of
grouped observations. The ML-VAE separates the latent representation into
semantically meaningful parts by working both at the group level and the
observation level, while retaining efficient test-time inference. Quantitative
and qualitative evaluations show that the ML-VAE model (i) learns a
semantically meaningful disentanglement of grouped data, (ii) enables
manipulation of the latent representation, and (iii) generalises to unseen
groups
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