In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly concentrate on the learning process in the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel Semantically Consistent Multi-view Representation Learning (SCMRL), which makes efforts to excavate underlying multi-view semantic consensus information and utilize the information to guide the unified feature representation learning. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module, which are elegantly integrated by the contrastive learning strategy to simultaneously align semantic labels of both view-specific feature representations and the learned unified feature representation. In this way, the consensus information in the semantic space can be effectively exploited to constrain the learning process of unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority.

翻译：在这项工作中,我们致力于未受监督的多视角代表制学习(UMRL)这一具有挑战性的任务,它要求以不受监督的方式从多种观点中学习统一的特征代表制,而现存的UMRL方法主要侧重于地物空间的学习过程,而忽视了不同观点中隐藏的宝贵语义信息。为了解决这一问题,我们提议了一部新颖的《具有真实性和一致性的多视角代表制学习》(《SCMRL)》),它努力挖掘多视角语义共识的基本信息,并利用这些信息指导统一特征代表制学习。具体地说,SCMRL由一个内观重建模块和一个统一的特征代表制学习模块组成,通过对比性学习战略优异地整合了这些模块,同时将特定观点代表制词的语义标签与所学的统一特征代表制统一起来。通过这种方式,可以有效地利用语系空间的共识信息来限制统一特征代表制的学习过程。与一些最先进的算法相比,广泛的实验显示了其优越性。</s>