4 papers · 1 filter
A Copula-Guided Temporal Dependency Method for Multitemporal Hyperspectral Images Unmixing
Ruiying Li, Bin Pan, Qiaoying Qu +2
Multitemporal hyperspectral unmixing (MTHU) aims to model variable endmembers and dynamical abundances, which emphasizes the critical temporal information. However, existing method…
SMILE: A Super-resolution Guided Multi-task Learning Method for Hyperspectral Unmixing
Ruiying Li, Bin Pan, Qiaoying Qu +2
The performance of hyperspectral unmixing may be constrained by low spatial resolution, which can be enhanced using super-resolution in a multitask learning way. However, integrati…
Preserving Domain Generalization in Fine-Tuning via Joint Parameter Selection
Bin Pan, Shiyu Shen, Zongbin Wang +2
Domain generalization seeks to develop models trained on a limited set of source domains that are capable of generalizing effectively to unseen target domains. While the predominan…
CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning
Xia Xu, Jochen Triesch
Self-supervised representation learning (SSRL) methods have shown great success in computer vision. In recent studies, augmentation-based contrastive learning methods have been pro…