  Feature generating networks face to the most important question, which is the
fitting difference (inconsistence) of the distribution between the generated
feature and the real data. This inconsistence further influence the performance
of the networks model, because training samples from seen classes is disjointed
with testing samples from unseen classes in zero-shot learning (ZSL). In
generalization zero-shot learning (GZSL), testing samples come from not only
seen classes but also unseen classes for closer to the practical situation.
Therefore, most of feature generating networks difficultly obtain satisfactory
performance for the challenging GZSL by adversarial learning the distribution
of semantic classes. To alleviate the negative influence of this inconsistence
for ZSL and GZSL, transfer feature generating networks with semantic classes
structure (TFGNSCS) is proposed to construct networks model for improving the
performance of ZSL and GZSL. TFGNSCS can not only consider the semantic
structure relationship between seen and unseen classes, but also learn the
difference of generating features by transferring classification model
information from seen to unseen classes in networks. The proposed method can
integrate the transfer loss, the classification loss and the Wasserstein
distance loss to generate enough CNN features, on which softmax classifiers are
trained for ZSL and GZSL. Experiments demonstrate that the performance of
TFGNSCS outperforms that of the state of the arts on four challenging datasets,
which are CUB,FLO,SUN, AWA in GZSL.
