Generative Dual Adversarial Network for Generalized Zero-shot Learning

CVPR 2019 He HuangChanghu WangPhilip S. YuChang-Dong Wang

This paper studies the problem of generalized zero-shot learning which requires the model to train on image-label pairs from some seen classes and test on the task of classifying new images from both seen and unseen classes. Most previous models try to learn a fixed one-directional mapping between visual and semantic space, while some recently proposed generative methods try to generate image features for unseen classes so that the zero-shot learning problem becomes a traditional fully-supervised classification problem... (read more)

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