In generalized zero shot learning (GZSL), the set of classes are split into
seen and unseen classes, where training relies on the semantic features of the
seen and unseen classes and the visual representations of only the seen
classes, while testing uses the visual representations of the seen and unseen
classes. Current methods address GZSL by learning a transformation from the
visual to the semantic space, exploring the assumption that the distribution of
classes in the semantic and visual spaces is relatively similar. Such methods
tend to transform unseen testing visual representations into one of the seen
classes' semantic features instead of the semantic features of the correct
unseen class, resulting in low accuracy GZSL classification. Recently,
generative adversarial networks (GAN) have been explored to synthesize visual
representations of the unseen classes from their semantic features - the
synthesized representations of the seen and unseen classes are then used to
train the GZSL classifier. This approach has been shown to boost GZSL
classification accuracy, however, there is no guarantee that synthetic visual
representations can generate back their semantic feature in a multi-modal
cycle-consistent manner. This constraint can result in synthetic visual
representations that do not represent well their semantic features. In this
paper, we propose the use of such constraint based on a new regularization for
the GAN training that forces the generated visual features to reconstruct their
original semantic features. Once our model is trained with this multi-modal
cycle-consistent semantic compatibility, we can then synthesize more
representative visual representations for the seen and, more importantly, for
the unseen classes. Our proposed approach shows the best GZSL classification
results in the field in several publicly available datasets.

本研究提出了基于对生成对抗网络训练的新规则的多模态循环一致性语义兼容性的方法，可以用于合成更具代表性的视觉表示，从而在几个公开可用的数据集中展现出最佳的广义零样本学习分类结果。