Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hybrid VAE: Improving Deep Generative Models using Partial Observations   Deep neural network models trained on large labeled datasets are the
state-of-the-art in a large variety of computer vision tasks. In many
applications, however, labeled data is expensive to obtain or requires a time
consuming manual annotation process. In contrast, unlabeled data is often
abundant and available in large quantities. We present a principled framework
to capitalize on unlabeled data by training deep generative models on both
labeled and unlabeled data. We show that such a combination is beneficial
because the unlabeled data acts as a data-driven form of regularization,
allowing generative models trained on few labeled samples to reach the
performance of fully-supervised generative models trained on much larger
datasets. We call our method Hybrid VAE (H-VAE) as it contains both the
generative and the discriminative parts. We validate H-VAE on three large-scale
datasets of different modalities: two face datasets: (MultiPIE, CelebA) and a
hand pose dataset (NYU Hand Pose). Our qualitative visualizations further
support improvements achieved by using partial observations.

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The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex