Most existing zero-shot learning methods consider the problem as a visual semantic embedding one. Given the demonstrated capability of Generative Adversarial Networks(GANs) to generate images, we instead leverage GANs to imagine unseen categories from text descriptions and hence recognize novel classes with no examples being seen. Specifically, we propose a simple yet effective generative model that takes as input noisy text descriptions about an unseen class (e.g.Wikipedia articles) and generates synthesized visual features for this class. With added pseudo data, zero-shot learning is naturally converted to a traditional classification problem. Additionally, to preserve the inter-class discrimination of the generated features, a visual pivot regularization is proposed as an explicit supervision. Unlike previous methods using complex engineered regularizers, our approach can suppress the noise well without additional regularization. Empirically, we show that our method consistently outperforms the state of the art on the largest available benchmarks on Text-based Zero-shot Learning.

利用生成对抗网络(GANs)想象从文本描述中无法见到的类别，生成目标类别的视觉特征，附加伪数据，使得零样本学习自然地转换为传统的分类问题，并提出视觉中心正则化以保持生成特征的跨类别区分能力。实验表明，该方法在基于文本的零样本学习的最大可用基准测试中始终优于现有技术水平。

基于生成对抗网络的噪声文本零样本学习方法