  Suffering from the extreme training data imbalance between seen and unseen
classes, most of existing state-of-the-art approaches fail to achieve
satisfactory results for the challenging generalized zero-shot learning task.
To circumvent the need for labeled examples of unseen classes, we propose a
novel generative adversarial network (GAN) that synthesizes CNN features
conditioned on class-level semantic information, offering a shortcut directly
from a semantic descriptor of a class to a class-conditional feature
distribution. Our proposed approach, pairing a Wasserstein GAN with a
classification loss, is able to generate sufficiently discriminative CNN
features to train softmax classifiers or any multimodal embedding method. Our
experimental results demonstrate a significant boost in accuracy over the state
of the art on five challenging datasets -- CUB, FLO, SUN, AWA and ImageNet --
in both the zero-shot learning and generalized zero-shot learning settings.
