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Existing methods using generative adversarial approaches for Zero-ShotLearning (ZSL) aim to generate realistic visual features from class semanticsby a single generative network, which is highly under-constrained. As a result,the previous methods cannot guarantee that the generated visual features cantruthfully reflect the corresponding semantics. To address this issue, wepropose a novel method named Cycle-consistent Adversarial Networks forZero-Shot Learning (CANZSL). It encourages a visual feature generator tosynthesize realistic visual features from semantics, and then inverselytranslate back synthesized the visual feature to corresponding semantic spaceby a semantic feature generator. Furthermore, in this paper a more challengingand practical ZSL problem is considered where the original semantics are fromnatural language with irrelevant words instead of clean semantics that arewidely used in previous work. Specifically, a multi-modal consistentbidirectional generative adversarial network is trained to handle unseeninstances by leveraging noise in the natural language. A forward one-to-manymapping from one text description to multiple visual features is coupled withan inverse many-to-one mapping from the visual space to the semantic space.Thus, a multi-modal cycle-consistency loss between the synthesized semanticrepresentations and the ground truth can be learned and leveraged to enforcethe generated semantic features to approximate to the real distribution insemantic space. Extensive experiments are conducted to demonstrate that ourmethod consistently outperforms state-of-the-art approaches on naturallanguage-based zero-shot learning tasks.

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