Rethinking Multi-view Representation Learning via Distilled Disentangling
<|reference_start|>Rethinking Multi-view Representation Learning via Distilled Disentangling: Multi-view representation learning aims to derive robust representations that are both view-consistent and view-specific from diverse data sources. This paper presents an in-depth analysis of existing approaches in this domain, highlighting a commonly overlooked aspect: the redundancy between view-consistent and view-specific representations. To this end, we propose an innovative framework for multi-view representation learning, which incorporates a technique we term 'distilled disentangling'. Our method introduces the concept of masked cross-view prediction, enabling the extraction of compact, high-quality view-consistent representations from various sources without incurring extra computational overhead. Additionally, we develop a distilled disentangling module that efficiently filters out consistency-related information from multi-view representations, resulting in purer view-specific representations. This approach significantly reduces redundancy between view-consistent and view-specific representations, enhancing the overall efficiency of the learning process. Our empirical evaluations reveal that higher mask ratios substantially improve the quality of view-consistent representations. Moreover, we find that reducing the dimensionality of view-consistent representations relative to that of view-specific representations further refines the quality of the combined representations. Our code is accessible at: https://github.com/Guanzhou-Ke/MRDD.<|reference_end|>
@article{ke2024rethinking,
    title={Rethinking Multi-view Representation Learning via Distilled
  Disentangling},
    author={Guanzhou Ke, Bo Wang, Xiaoli Wang, and Shengfeng He},
    journal={arXiv preprint arXiv:2403.10897},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10897},
    primaryClass={cs.CV cs.MM}
}