Paper

CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language

Existing methods using generative adversarial approaches for Zero-Shot Learning (ZSL) aim to generate realistic visual features from class semantics by a single generative network, which is highly under-constrained. As a result, the previous methods cannot guarantee that the generated visual features can truthfully reflect the corresponding semantics... (read more)

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