Title: Zero-Shot Learning via Class-Conditioned Deep Generative Models,
Abstract: We present a deep generative model for learning to predict classes not seen
at training time. Unlike most existing methods for this problem, that represent
each class as a point (via a semantic embedding), we represent each seen/unseen
class using a class-specific latent-space distribution, conditioned on class
attributes. We use these latent-space distributions as a prior for a supervised
variational autoencoder (VAE), which also facilitates learning highly
discriminative feature representations for the inputs. The entire framework is
learned end-to-end using only the seen-class training data. The model infers
corresponding attributes of a test image by maximizing the VAE lower bound; the
inferred attributes may be linked to labels not seen when training. We further
extend our model to a (1) semi-supervised/transductive setting by leveraging
unlabeled unseen-class data via an unsupervised learning module, and (2)
few-shot learning where we also have a small number of labeled inputs from the
unseen classes. We compare our model with several state-of-the-art methods
through a comprehensive set of experiments on a variety of benchmark data sets.