Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

The domain generalization approach seeks to develop a universal model that performs well
on unknown target domains with the aid of diverse source domains. Data augmentation has …

This article provides a novel framework for single-domain generalized object detection (ie,
Single-DGOD), where we are interested in learning and maintaining the semantic structures …

Domain generalization (DG) is a prevalent problem in real-world applications, which aims to
train well-generalized models for unseen target domains by utilizing several source …

Source-Free Domain Generalization (SFDG) aims to develop a model that works for unseen
target domains without relying on any source domain. Research in SFDG primarily bulids …

Federated learning (FL) enables distributed clients to collaboratively learn a global model,
suggesting its potential for use in improving data privacy in machine learning. However …

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 …

Domain generalization (DG) aims to train a model with access to a limited number of source
domains for generalizing it across various unseen target domains. The key to solving the DG …

Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the
target domain during inference. One popular approach involves fine-tuning model with cross …

This paper presents a Domain-Inspired Sharpness-Aware Minimization (DISAM) algorithm
for optimization under domain shifts. It is motivated by the inconsistent convergence degree …