Generalized　zero-shot　learning　(GZSL)　requires　that　models　are　able　to　recognize　classes　they　were　trained　on,　and　new　classes　they　haven＇t　seen　before.　Feature-generation　approaches　are　popular　due　to　their　effectiveness　in　mitigating　overfitting　to　the　training　classes.　Existing　generative　approaches　usually　adopt　simple　discriminators　for　distribution　or　classification　supervision,　however,　thus　limiting　their　ability　to　generate　visual　features　that　are　discriminative　of　and　transferable　to　novel　categories.　To　overcome　this　limitation　and　improve　the　quality　of　generated　features,　we　propose　a　dual　prototype　contrastive　augmented　discriminator　for　the　generative　adversarial　network.　Specifically,　we　design　a　Dual　Prototype　Contrastive　Network　(DPCN),　which　leverages　complementary　information　between　visual　space　and　semantic　space　through　multi-task　prototype　contrastive　learning.　Contrastive　learning　of　the　visual　prototypes　enhances　the　ability　of　the　generated　features　to　distinguish　between　classes,　while　the　contrastive　learning　of　the　semantic　prototypes　improves　their　transferability.　Furthermore,　we　introduce　margins　into　the　contrastive　learning　process　to　ensure　both　intra-class　compactness　and　inter-class　separation.　To　demonstrate　the　effectiveness　of　the　proposed　approach,　we　conduct　experiments　on　three　widely-used　zero-shot　learning　benchmark　datasets,　where　DPCN　achieves　state-of-the-art　performance　for　GZSL.