Domain Invariant

Domain-invariant learning aims to develop machine learning models robust to variations in data distribution across different domains, enabling better generalization to unseen data. Current research focuses on techniques like contrastive learning, Bayesian methods, and attention mechanisms within various architectures, including Gaussian processes and neural networks, to extract domain-invariant features. This field is crucial for improving the reliability and applicability of machine learning models in real-world scenarios where data distributions are inherently heterogeneous, impacting diverse applications such as face recognition, robotics, and activity recognition. The development of effective domain-invariant learning methods is essential for building more robust and generalizable AI systems.