时间：2026-09-22
本文（Multiview Self-Representation Learning across Heterogeneous Views）原载Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS 2026)，由四川大学王竹教授等科研人员创作，系四川大学市场经济法治研究所系列学术成果。后续会持续分享四川大学市场经济法治研究所系列学术成果，欢迎大家阅读。
Features of the same sample generated by different pretrained models often exhibit inherently distinct feature distributions. Learning invariant representations from large-scale unlabeled visual data in a fully unsupervised transfer manner remains a significant challenge. In this paper, we propose a multiview self-representation learning (MSRL) method in which invariant representations are learned by exploiting the self-representation property of features across heterogeneous views. The features are derived from large-scale unlabeled visual data through transfer learning with various pretrained models and are referred to as heterogeneous multiview data. We introduce an information-passing mechanism that relies on self-representation learning to support feature aggregation over the outputs of the linear model. Moreover, an assignment probability distribution consistency scheme is presented to guide multiview self-representation learning by exploiting complementary information across different views. Consequently, representation invariance across different linear models is enforced through this scheme. Additionally, we provide a theoretical analysis of the assignment probability distribution consistency and the incremental views. Extensive experiments demonstrate that the proposed MSRL method consistently outperforms several state-of-the-art approaches.
Jie Chen, Yuanbiao Gou, Chuanbin Liu, Zhu Wang, and- Xi Peng: Multiview Self-Representation Learning across Heterogeneous Views, Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS 2026), Sydney, Australia, Dec. 8-Dec. 10, 2026.（论文下载）
