Dual prototype contrastive network for generalized zero-shot learning

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.