  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.
