Domain Representation

Domain representation research focuses on creating feature representations that generalize across different data domains, overcoming challenges posed by variations in data distribution and label spaces. Current efforts concentrate on developing robust algorithms, often leveraging contrastive learning and transformer-based architectures, to learn domain-invariant or domain-general features, sometimes incorporating knowledge graphs or textual semantics to improve alignment and reduce spurious correlations. This work is crucial for improving the robustness and reliability of machine learning models in real-world applications where data may be heterogeneous and incomplete, impacting fields like medical image analysis and person search. The ultimate goal is to build more adaptable and generalizable AI systems.