934 research outputs found
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Given datasets from multiple domains, a key challenge is to efficiently
exploit these data sources for modeling a target domain. Variants of this
problem have been studied in many contexts, such as cross-domain translation
and domain adaptation. We propose AlignFlow, a generative modeling framework
that models each domain via a normalizing flow. The use of normalizing flows
allows for a) flexibility in specifying learning objectives via adversarial
training, maximum likelihood estimation, or a hybrid of the two methods; and b)
learning and exact inference of a shared representation in the latent space of
the generative model. We derive a uniform set of conditions under which
AlignFlow is marginally-consistent for the different learning objectives.
Furthermore, we show that AlignFlow guarantees exact cycle consistency in
mapping datapoints from a source domain to target and back to the source
domain. Empirically, AlignFlow outperforms relevant baselines on image-to-image
translation and unsupervised domain adaptation and can be used to
simultaneously interpolate across the various domains using the learned
representation.Comment: AAAI 202
C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds
Flow-based generative models have highly desirable properties like exact
log-likelihood evaluation and exact latent-variable inference, however they are
still in their infancy and have not received as much attention as alternative
generative models. In this paper, we introduce C-Flow, a novel conditioning
scheme that brings normalizing flows to an entirely new scenario with great
possibilities for multi-modal data modeling. C-Flow is based on a parallel
sequence of invertible mappings in which a source flow guides the target flow
at every step, enabling fine-grained control over the generation process. We
also devise a new strategy to model unordered 3D point clouds that, in
combination with the conditioning scheme, makes it possible to address 3D
reconstruction from a single image and its inverse problem of rendering an
image given a point cloud. We demonstrate our conditioning method to be very
adaptable, being also applicable to image manipulation, style transfer and
multi-modal image-to-image mapping in a diversity of domains, including RGB
images, segmentation maps, and edge masks
Density Matching for Bilingual Word Embedding
Recent approaches to cross-lingual word embedding have generally been based
on linear transformations between the sets of embedding vectors in the two
languages. In this paper, we propose an approach that instead expresses the two
monolingual embedding spaces as probability densities defined by a Gaussian
mixture model, and matches the two densities using a method called normalizing
flow. The method requires no explicit supervision, and can be learned with only
a seed dictionary of words that have identical strings. We argue that this
formulation has several intuitively attractive properties, particularly with
the respect to improving robustness and generalization to mappings between
difficult language pairs or word pairs. On a benchmark data set of bilingual
lexicon induction and cross-lingual word similarity, our approach can achieve
competitive or superior performance compared to state-of-the-art published
results, with particularly strong results being found on etymologically distant
and/or morphologically rich languages.Comment: Accepted by NAACL-HLT 201
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