DASCN uses a unified primal-dual GAN architecture to generate semantics-consistent visual features for generalized zero-shot learning, claiming state-of-the-art gains.
An empirical study and analysis of generalized zero-shot learning for object recognition in the wild
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Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning
DASCN uses a unified primal-dual GAN architecture to generate semantics-consistent visual features for generalized zero-shot learning, claiming state-of-the-art gains.