  A novel variational autoencoder is developed to model images, as well as
associated labels or captions. The Deep Generative Deconvolutional Network
(DGDN) is used as a decoder of the latent image features, and a deep
Convolutional Neural Network (CNN) is used as an image encoder; the CNN is used
to approximate a distribution for the latent DGDN features/code. The latent
code is also linked to generative models for labels (Bayesian support vector
machine) or captions (recurrent neural network). When predicting a
label/caption for a new image at test, averaging is performed across the
distribution of latent codes; this is computationally efficient as a
consequence of the learned CNN-based encoder. Since the framework is capable of
modeling the image in the presence/absence of associated labels/captions, a new
semi-supervised setting is manifested for CNN learning with images; the
framework even allows unsupervised CNN learning, based on images alone.
