Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Weakly supervised whole slide image classification is usually formulated as a multiple
instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut …

Domain generalization (DG) endeavours to develop robust models that possess strong
generalizability while preserving excellent discriminability. Nonetheless, pivotal DG …

Federated domain adaptation (FDA) is an effective method for performing learning tasks
over distributed networks, which well improves data privacy and portability in unsupervised …

Abstract Federated Domain Generalization aims to learn a domain-invariant model from
multiple decentralized source domains for deployment on unseen target domain. Due to …

Domain generalization (DG) aims to learn from multiple known source domains a model that
can generalize well to unknown target domains. The existing DG methods usually exploit the …

Self-supervised learning (SSL) has emerged as a promising approach for learning
representations from unlabeled data. Momentum-based contrastive frameworks such as …

Federated learning (FL) enables multiple data sources to collaboratively train a global
model for Multi-source Visual Fusion and Understanding (MSVFU) without centralizing raw …

As a main branch of domain adaptation (DA), multi-source DA (MSDA) has attracted
increasing attention for exploiting information from multi-source domain data. However, how …

Multi-source domain adaptation aims to transfer knowledge from multiple labeled source
domains to an unlabeled target domain and reduce the domain shift. Considering data …