  Bidirectional mapping-based generalized zero-shot learning (GZSL) methods
rely on the quality of synthesized features to recognize seen and unseen data.
Therefore, learning a joint distribution of seen-unseen domains and preserving
domain distinction is crucial for these methods. However, existing methods only
learn the underlying distribution of seen data, although unseen class semantics
are available in the GZSL problem setting. Most methods neglect retaining
domain distinction and use the learned distribution to recognize seen and
unseen data. Consequently, they do not perform well. In this work, we utilize
the available unseen class semantics alongside seen class semantics and learn
joint distribution through a strong visual-semantic coupling. We propose a
bidirectional mapping coupled generative adversarial network (BMCoGAN) by
extending the coupled generative adversarial network into a dual-domain
learning bidirectional mapping model. We further integrate a Wasserstein
generative adversarial optimization to supervise the joint distribution
learning. We design a loss optimization for retaining domain distinctive
information in the synthesized features and reducing bias towards seen classes,
which pushes synthesized seen features towards real seen features and pulls
synthesized unseen features away from real seen features. We evaluate BMCoGAN
on benchmark datasets and demonstrate its superior performance against
contemporary methods.
