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Learning Adaptive and Visually Aligned Conditions for Generative Zero-Shot Learning
Generative zero-shot learning (ZSL) synthesizes visual features for unseen classes by learning a semantic-conditioned generator from seen classes. Since semantic conditions determine the knowledge transfer from seen to unseen classes, their effectiveness is critical for generating plausible features
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