  Conventional zero-shot learning (ZSL) methods generally learn an embedding,
e.g., visual-semantic mapping, to handle the unseen visual samples via an
indirect manner. In this paper, we take the advantage of generative adversarial
networks (GANs) and propose a novel method, named leveraging invariant side GAN
(LisGAN), which can directly generate the unseen features from random noises
which are conditioned by the semantic descriptions. Specifically, we train a
conditional Wasserstein GANs in which the generator synthesizes fake unseen
features from noises and the discriminator distinguishes the fake from real via
a minimax game. Considering that one semantic description can correspond to
various synthesized visual samples, and the semantic description, figuratively,
is the soul of the generated features, we introduce soul samples as the
invariant side of generative zero-shot learning in this paper. A soul sample is
the meta-representation of one class. It visualizes the most
semantically-meaningful aspects of each sample in the same category. We
regularize that each generated sample (the varying side of generative ZSL)
should be close to at least one soul sample (the invariant side) which has the
same class label with it. At the zero-shot recognition stage, we propose to use
two classifiers, which are deployed in a cascade way, to achieve a
coarse-to-fine result. Experiments on five popular benchmarks verify that our
proposed approach can outperform state-of-the-art methods with significant
improvements.
