  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. Furthermore, in this paper a more challenging
and practical ZSL problem is considered where the original semantics are from
natural language with irrelevant words instead of clean semantics that are
widely used in previous work. Specifically, a multi-modal consistent
bidirectional generative adversarial network is trained to handle unseen
instances by leveraging noise in the natural language. A forward one-to-many
mapping from one text description to multiple visual features is coupled with
an inverse many-to-one mapping from the visual space to the semantic space.
Thus, a multi-modal cycle-consistency loss between the synthesized semantic
representations and the ground truth can be learned and leveraged to enforce
the generated semantic features to approximate to the real distribution in
semantic space. Extensive experiments are conducted to demonstrate that our
method consistently outperforms state-of-the-art approaches on natural
language-based zero-shot learning tasks.
