  The paradigm shift from shallow classifiers with hand-crafted features to
end-to-end trainable deep learning models has shown significant improvements on
supervised learning tasks. Despite the promising power of deep neural networks
(DNN), how to alleviate overfitting during training has been a research topic
of interest. In this paper, we present a Generative-Discriminative Variational
Model (GDVM) for visual classification, in which we introduce a latent variable
inferred from inputs for exhibiting generative abilities towards prediction. In
other words, our GDVM casts the supervised learning task as a generative
learning process, with data discrimination to be jointly exploited for improved
classification. In our experiments, we consider the tasks of multi-class
classification, multi-label classification, and zero-shot learning. We show
that our GDVM performs favorably against the baselines or recent generative DNN
models.
