The field of unsupervised domain generalization and representation learning is moving towards developing more robust and generalizable models. Researchers are exploring new techniques to enhance the generalization ability of deep learning models in unsupervised settings, such as learning minimal sufficient semantic representations and leveraging data-intrinsic regularization frameworks. These approaches aim to reduce out-of-distribution risk and improve representation learning across various tasks. Notable papers in this area include:

• Minimal Semantic Sufficiency Meets Unsupervised Domain Generalization, which proposes a new state-of-the-art method for unsupervised domain generalization.
• Self Identity Mapping, which introduces a simple yet effective regularization framework that can be seamlessly integrated into different network architectures and tasks.