Generalized zero shot classification aims to recognize both seen and unseen samples in test sets, which has gained great attention. Recently, many works consider using generative adversarial network to generate unseen samples for solving generalized zero shot classification problem. In this paper, we study how to generate discriminative and meaningful samples. We propose a method to learn discriminative and meaningful samples for generalized zero shot classification tasks (LDMS) by generative adversarial network with the regularization of class consistency and semantic consistency. In order to make the generated samples discriminative, class consistency is used, such that the generated samples of the same classes are near and of different classes are far away. In order to make the generated samples meaningful, semantic consistency is used, such that the semantic representations of the generated samples are close to their class prototypes. It encodes the discriminative information and semantic information to the generator. In order to alleviate the bias problem, we select some confident unseen samples. We use the seen samples, the generated unseen samples and the selected confident unseen samples to train the final classifier. Extensive experiments on all datasets demonstrate that the proposed method can outperform state-of-the-art models on generalized zero shot classification tasks.

广义零镜头分类旨在识别测试集中的可见样本和不可见样本，因此备受关注。最近，许多作品考虑使用生成对抗网络来生成看不见的样本，以解决广义零击分类问题。在本文中，我们研究了如何生成具有判别力和有意义的样本。我们提出一种方法，通过生成对抗网络，利用类一致性和语义一致性的正则化方法来学习用于广义零镜头分类任务（LDMS）的判别和有意义样本。为了使生成的样本具有区别性，使用了类别一致性，以使相同类别的生成样本很近，而不同类别的生成样本很远。为了使生成的样本有意义，使用了语义一致性，这样生成的样本的语义表示就接近其类原型。它将判别信息和语义信息编码到生成器。为了缓解偏差问题，我们选择了一些自信的，看不见的样本。我们使用可见样本，生成的看不见样本和选定的置信看不见样本来训练最终分类器。在所有数据集上进行的大量实验表明，该方法在广义零镜头分类任务上的性能优于最新模型。生成的看不见样本和选定的有信心看不见样本来训练最终分类器。在所有数据集上进行的大量实验表明，该方法在广义零镜头分类任务上的性能优于最新模型。生成的看不见样本和选定的有信心看不见样本来训练最终分类器。在所有数据集上进行的大量实验表明，该方法在广义零镜头分类任务上的性能优于最新模型。