GT-GAN: A General Transductive Zero-shot Learning Method Based on GAN

Most zero-shot learning (ZSL) methods based on generative adversarial networks (GANs) utilize random noise and semantic descriptions to synthesize image features of unseen classes, which alleviates the problem of training data imbalance between seen and unseen classes. However, these methods usually only learn the distributions of seen classes in the training stage, ignoring the unseen ones. Due to the different distributions of seen and unseen samples, i.e., image features, these methods cannot generate unseen features of sufficient quality, so the performances are also limited, especially for the generalized zero-shot learning (GZSL) setting. In this article, we propose a general transductive method based on GANs, called GT-GAN, which can improve the quality of generated unseen image features and therefore benefit the classification. A new loss function is introduced to make the relative positions between each unseen image and its $k$ nearest neighbors in the feature space as consistent as possible with their relative positions in the semantic space; this loss function may be easily applied in most existing GAN-based models. Experimental results on five benchmark datasets show a significant improvement in accuracy compared with that of original models, especially in the GZSL setting.