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Deep neural network models trained on large labeled datasets are thestate-of-the-art in a large variety of computer vision tasks. In manyapplications, however, labeled data is expensive to obtain or requires a timeconsuming manual annotation process. In contrast, unlabeled data is oftenabundant and available in large quantities. We present a principled frameworkto capitalize on unlabeled data by training deep generative models on bothlabeled and unlabeled data. We show that such a combination is beneficialbecause the unlabeled data acts as a data-driven form of regularization,allowing generative models trained on few labeled samples to reach theperformance of fully-supervised generative models trained on much largerdatasets. We call our method Hybrid VAE (H-VAE) as it contains both thegenerative and the discriminative parts. We validate H-VAE on three large-scaledatasets of different modalities: two face datasets: (MultiPIE, CelebA) and ahand pose dataset (NYU Hand Pose). Our qualitative visualizations furthersupport improvements achieved by using partial observations.

Hybrid VAE: Improving Deep Generative Models using Partial Observations