The rapid development of Vision Foundation Model (VFM) brings inherent out-domain
generalization for a variety of down-stream tasks. Among them, domain generalized …

Domain generalization (DG) aims to enhance the ability of models trained on source
domains to generalize effectively to unseen domains. Recently, Sharpness-Aware …

Domain Generalization (DG) aims to enable models to generalize to unseen target domains
by learning from multiple source domains. Existing DG methods primarily rely on …

Single domain generalization aims to train a model that can generalize well to multiple
unseen target domains by leveraging the knowledge in a related source domain. Recent …

Deep learning models are usually black boxes when deployed on machine learning
platforms. Prior works have shown that the attributes (eg, the number of convolutional layers) …

Domain generalization (DG) aims to learn knowledge from multiple related domains to
achieve a robust generalization performance in unseen target domains, which is an effective …

Federated domain generalization (FedDG) aims to improve the global model generalization
in unseen domains by addressing data heterogeneity under privacy-preserving constraints …

Cross-Domain Recommendation (CDR) have received widespread attention due to their
ability to utilize rich information across domains. However, most existing CDR methods …

Machine translation (MT) is the automatic, nonhuman translation of one text into another
within the field of computer languages. However, as people used a variety of texts, it is …

The purpose of domain generalization is to develop models that exhibit a higher degree of
generality, meaning they perform better when evaluated on data coming from previously …