  Generalized zero-shot learning (GZSL) is a challenging class of vision and
knowledge transfer problems in which both seen and unseen classes appear during
testing. Existing GZSL approaches either suffer from semantic loss and discard
discriminative information at the embedding stage, or cannot guarantee the
visual-semantic interactions. To address these limitations, we propose the Dual
Adversarial Semantics-Consistent Network (DASCN), which learns primal and dual
Generative Adversarial Networks (GANs) in a unified framework for GZSL. In
particular, the primal GAN learns to synthesize inter-class discriminative and
semantics-preserving visual features from both the semantic representations of
seen/unseen classes and the ones reconstructed by the dual GAN. The dual GAN
enforces the synthetic visual features to represent prior semantic knowledge
well via semantics-consistent adversarial learning. To the best of our
knowledge, this is the first work that employs a novel dual-GAN mechanism for
GZSL. Extensive experiments show that our approach achieves significant
improvements over the state-of-the-art approaches.
