Generalized zero-shot learning (GZSL) is a challenging class of vision and knowledge transfer problems in which both seen and unseen classes appear during testing. Existing GZSL approaches either suffer from semantic loss and discard discriminative information at the embedding stage, or cannot guarantee the visual-semantic interactions. To address these limitations, we propose the Dual Adversarial Semantics-Consistent Network (DASCN), which learns primal and dual Generative Adversarial Networks (GANs) in a unified framework for GZSL. In particular, the primal GAN learns to synthesize inter-class discriminative and semantics-preserving visual features from both the semantic representations of seen/unseen classes and the ones reconstructed by the dual GAN. The dual GAN enforces the synthetic visual features to represent prior semantic knowledge well via semantics-consistent adversarial learning. To the best of our knowledge, this is the first work that employs a novel dual-GAN mechanism for GZSL. Extensive experiments show that our approach achieves significant improvements over the state-of-the-art approaches.

本文提出了一种名为Dual Adversarial Semantics-Consistent Network (DASCN)的机制，该机制采用双重 GAN 对视觉语义交互传递的问题进行了处理， 并在广义零样本学习(GZSL)问题上实现了先前算法所未能完成的任务。

双重对抗一致语义网络用于广义零样本学习