2,165 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
Hybrid Models with Deep and Invertible Features
We propose a neural hybrid model consisting of a linear model defined on a
set of features computed by a deep, invertible transformation (i.e. a
normalizing flow). An attractive property of our model is that both
p(features), the density of the features, and p(targets | features), the
predictive distribution, can be computed exactly in a single feed-forward pass.
We show that our hybrid model, despite the invertibility constraints, achieves
similar accuracy to purely predictive models. Moreover the generative component
remains a good model of the input features despite the hybrid optimization
objective. This offers additional capabilities such as detection of
out-of-distribution inputs and enabling semi-supervised learning. The
availability of the exact joint density p(targets, features) also allows us to
compute many quantities readily, making our hybrid model a useful building
block for downstream applications of probabilistic deep learning.Comment: ICML 201
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