39 research outputs found
Multi-Source Neural Variational Inference
Learning from multiple sources of information is an important problem in
machine-learning research. The key challenges are learning representations and
formulating inference methods that take into account the complementarity and
redundancy of various information sources. In this paper we formulate a
variational autoencoder based multi-source learning framework in which each
encoder is conditioned on a different information source. This allows us to
relate the sources via the shared latent variables by computing divergence
measures between individual source's posterior approximations. We explore a
variety of options to learn these encoders and to integrate the beliefs they
compute into a consistent posterior approximation. We visualise learned beliefs
on a toy dataset and evaluate our methods for learning shared representations
and structured output prediction, showing trade-offs of learning separate
encoders for each information source. Furthermore, we demonstrate how conflict
detection and redundancy can increase robustness of inference in a multi-source
setting.Comment: AAAI 2019, Association for the Advancement of Artificial Intelligence
(AAAI) 201
Wyner VAE: Joint and Conditional Generation with Succinct Common Representation Learning
A new variational autoencoder (VAE) model is proposed that learns a succinct
common representation of two correlated data variables for conditional and
joint generation tasks. The proposed Wyner VAE model is based on two
information theoretic problems---distributed simulation and channel
synthesis---in which Wyner's common information arises as the fundamental limit
of the succinctness of the common representation. The Wyner VAE decomposes a
pair of correlated data variables into their common representation (e.g., a
shared concept) and local representations that capture the remaining randomness
(e.g., texture and style) in respective data variables by imposing the mutual
information between the data variables and the common representation as a
regularization term. The utility of the proposed approach is demonstrated
through experiments for joint and conditional generation with and without style
control using synthetic data and real images. Experimental results show that
learning a succinct common representation achieves better generative
performance and that the proposed model outperforms existing VAE variants and
the variational information bottleneck method.Comment: 24 pages, 18 figure
SCAN: Learning Hierarchical Compositional Visual Concepts
The seemingly infinite diversity of the natural world arises from a
relatively small set of coherent rules, such as the laws of physics or
chemistry. We conjecture that these rules give rise to regularities that can be
discovered through primarily unsupervised experiences and represented as
abstract concepts. If such representations are compositional and hierarchical,
they can be recombined into an exponentially large set of new concepts. This
paper describes SCAN (Symbol-Concept Association Network), a new framework for
learning such abstractions in the visual domain. SCAN learns concepts through
fast symbol association, grounding them in disentangled visual primitives that
are discovered in an unsupervised manner. Unlike state of the art multimodal
generative model baselines, our approach requires very few pairings between
symbols and images and makes no assumptions about the form of symbol
representations. Once trained, SCAN is capable of multimodal bi-directional
inference, generating a diverse set of image samples from symbolic descriptions
and vice versa. It also allows for traversal and manipulation of the implicit
hierarchy of visual concepts through symbolic instructions and learnt logical
recombination operations. Such manipulations enable SCAN to break away from its
training data distribution and imagine novel visual concepts through
symbolically instructed recombination of previously learnt concepts