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Feature generating networks face to the most important question, which is thefitting difference (inconsistence) of the distribution between the generatedfeature and the real data. This inconsistence further influence the performanceof the networks model, because training samples from seen classes is disjointedwith testing samples from unseen classes in zero-shot learning (ZSL). Ingeneralization zero-shot learning (GZSL), testing samples come from not onlyseen classes but also unseen classes for closer to the practical situation.Therefore, most of feature generating networks difficultly obtain satisfactoryperformance for the challenging GZSL by adversarial learning the distributionof semantic classes. To alleviate the negative influence of this inconsistencefor ZSL and GZSL, transfer feature generating networks with semantic classesstructure (TFGNSCS) is proposed to construct networks model for improving theperformance of ZSL and GZSL. TFGNSCS can not only consider the semanticstructure relationship between seen and unseen classes, but also learn thedifference of generating features by transferring classification modelinformation from seen to unseen classes in networks. The proposed method canintegrate the transfer loss, the classification loss and the Wassersteindistance loss to generate enough CNN features, on which softmax classifiers aretrained for ZSL and GZSL. Experiments demonstrate that the performance ofTFGNSCS outperforms that of the state of the arts on four challenging datasets,which are CUB,FLO,SUN, AWA in GZSL.

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