Transfer feature generating networks with semantic classes structure for zero-shot learning

Suffering from the generating feature inconsistence of seen classes training model for following the distribution of unseen classes , most of existing feature generating networks difficultly obtain satisfactory performance for the challenging generalization zero-shot learning (GZSL) by adversarial learning the distribution of semantic classes. To alleviate the negative influence of this inconsistence for zero-shot learning (ZSL), 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 balancing transfer information between seen and unseen classes in networks. The proposed method can integrate a Wasserstein generative adversarial network with classification loss and transfer loss to generate enough CNN feature, 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.