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Abstract Zero-shot learning (ZSL) addresses the unseen class recognition problem by leveraging semantic information to transfer knowledge from seen classes to unseen classes. Most existing models rely on learning an embedding space where the classes of test samples according to sample-prototype similarities are determined. However, most approaches do not explore the underlying distribution structure of embedded samples.
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