  We propose an optimal transport (OT) framework for generalized zero-shot
learning (GZSL), seeking to distinguish samples for both seen and unseen
classes, with the assist of auxiliary attributes. The discrepancy between
features and attributes is minimized by solving an optimal transport problem.
{Specifically, we build a conditional generative model to generate features
from seen-class attributes, and establish an optimal transport between the
distribution of the generated features and that of the real features.} The
generative model and the optimal transport are optimized iteratively with an
attribute-based regularizer, that further enhances the discriminative power of
the generated features. A classifier is learned based on the features generated
for both the seen and unseen classes. In addition to generalized zero-shot
learning, our framework is also applicable to standard and transductive ZSL
problems. Experiments show that our optimal transport-based method outperforms
state-of-the-art methods on several benchmark datasets.
