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

Understand

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.
• To address this issue, we propose a novel method named Cycle-consistent Adversarial Networks for Zero-Shot Learning (CANZSL).
• It encourages a visual feature generator to synthesize realistic visual features from semantics, and then inversely translate back synthesized the visual feature to corresponding semantic space by a semantic feature generator.