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：本文致力于无监督多视图表示学习（UMRL）这一具有挑战性的任务，该任务要求以无监督方式从多个视图中学习统一的特征表示。现有UMRL方法主要关注特征空间中的学习过程，而忽视了不同视图中隐藏的宝贵语义信息。为解决这一问题，我们提出了一种新颖的语义一致多视图表示学习方法（SCMRL），该方法致力于挖掘潜在的多视图语义共识信息，并利用这些信息指导统一的特征表示学习。具体而言，SCMRL包含视图内重建模块和统一特征表示学习模块，两者通过对比学习策略巧妙整合，同时对齐视图特定特征表示与学习得到的统一特征表示的语义标签。通过这种方式，语义空间中的共识信息可被有效利用以约束统一特征表示的学习过程。与若干最先进算法的广泛实验对比表明，该方法具有显著优越性。