Generalized zero-shot classification (GZSC) is a challenging task to recognize seen and unseen samples from target domain by seen samples in source domain. Since the lack of unseen data, many methods train a generative adversarial network (GAN) to generate unseen samples. However, the GAN model trained by seen samples is not suitable for generating unseen samples. For dealing with this problem, we train the GAN model by generating seen and unseen samples, simultaneously. In order to generate high-quality unseen samples, the visual prototypes of the generated unseen samples are made near to the real unseen visual prototypes. We select the confident unseen samples based on the agreement of the current two unseen classifiers and use them to update the unseen visual prototypes. Through the iteratively generating and selecting method (IGS), we can generate high-quality unseen samples and select the most confident unseen samples. Experimental results on the standard benchmarks show the superiority of the proposed model over the state-of-the-art methods for GZSC tasks.

广义零镜头分类（GZSC）是一项艰巨的任务，要通过源域中的可见样本识别目标域中的可见样本和不可见样本。由于缺少看不见的数据，因此许多方法训练了生成对抗网络（GAN）以生成看不见的样本。但是，由可见样本训练的GAN模型不适合生成看不见的样本。为了解决这个问题，我们通过同时生成可见和不可见样本来训练GAN模型。为了生成高质量的看不见的样本，将生成的看不见的样本的视觉原型制作成接近真实的看不见的视觉原型。我们基于当前两个看不见的分类器的协议选择有信心的看不见的样本，并使用它们来更新看不见的视觉原型。通过迭代生成和选择方法（IGS），我们可以生成高质量的看不见的样本，并选择最有信心的看不见的样本。在标准基准上的实验结果表明，该模型优于GZSC任务的最新方法。