3 papers
cs.LG2026
FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
Zhiqiang Kou, Junxiang Wu, Wenke Huang +8
Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints…
cs.CV2025
LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clustering
Shide Du, Chunming Wu, Zihan Fang +4
Deep anchor-based multi-view clustering methods enhance the scalability of neural networks by utilizing representative anchors to reduce the computational complexity of large-scale…
cs.CV2024
OpenViewer: Openness-Aware Multi-View Learning
Shide Du, Zihan Fang, Yanchao Tan +3
Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying…