  Zero-shot learning (ZSL) aims to recognize the novel classes which cannot be
collected for training a prediction model. Accordingly, generative models
(e.g., generative adversarial network (GAN)) are typically used to synthesize
the visual samples conditioned by the class semantic vectors and achieve
remarkable progress for ZSL. However, existing GAN-based generative ZSL methods
are based on hand-crafted models, which cannot adapt to various
datasets/scenarios and fails to model instability. To alleviate these
challenges, we propose evolutionary generative adversarial network search
(termed EGANS) to automatically design the generative network with good
adaptation and stability, enabling reliable visual feature sample synthesis for
advancing ZSL. Specifically, we adopt cooperative dual evolution to conduct a
neural architecture search for both generator and discriminator under a unified
evolutionary adversarial framework. EGANS is learned by two stages: evolution
generator architecture search and evolution discriminator architecture search.
During the evolution generator architecture search, we adopt a many-to-one
adversarial training strategy to evolutionarily search for the optimal
generator. Then the optimal generator is further applied to search for the
optimal discriminator in the evolution discriminator architecture search with a
similar evolution search algorithm. Once the optimal generator and
discriminator are searched, we entail them into various generative ZSL
baselines for ZSL classification. Extensive experiments show that EGANS
consistently improve existing generative ZSL methods on the standard CUB, SUN,
AWA2 and FLO datasets. The significant performance gains indicate that the
evolutionary neural architecture search explores a virgin field in ZSL.
