675 research outputs found
Learning Disentangled Representations with Reference-Based Variational Autoencoders
Learning disentangled representations from visual data, where different
high-level generative factors are independently encoded, is of importance for
many computer vision tasks. Solving this problem, however, typically requires
to explicitly label all the factors of interest in training images. To
alleviate the annotation cost, we introduce a learning setting which we refer
to as "reference-based disentangling". Given a pool of unlabeled images, the
goal is to learn a representation where a set of target factors are
disentangled from others. The only supervision comes from an auxiliary
"reference set" containing images where the factors of interest are constant.
In order to address this problem, we propose reference-based variational
autoencoders, a novel deep generative model designed to exploit the
weak-supervision provided by the reference set. By addressing tasks such as
feature learning, conditional image generation or attribute transfer, we
validate the ability of the proposed model to learn disentangled
representations from this minimal form of supervision
Guiding InfoGAN with Semi-Supervision
In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN)
for image synthesis that leverages information from few labels (as little as
0.22%, max. 10% of the dataset) to learn semantically meaningful and
controllable data representations where latent variables correspond to label
categories. The architecture builds on Information Maximizing Generative
Adversarial Networks (InfoGAN) and is shown to learn both continuous and
categorical codes and achieves higher quality of synthetic samples compared to
fully unsupervised settings. Furthermore, we show that using small amounts of
labeled data speeds-up training convergence. The architecture maintains the
ability to disentangle latent variables for which no labels are available.
Finally, we contribute an information-theoretic reasoning on how introducing
semi-supervision increases mutual information between synthetic and real data
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