Domain Alignment

Domain alignment in machine learning focuses on bridging the gap between data from different sources (domains), enabling models trained on one domain to generalize effectively to others. Current research emphasizes unsupervised domain adaptation, employing techniques like adversarial training, optimal transport, and contrastive learning within various architectures including convolutional neural networks and transformers, to achieve domain-invariant feature representations. This work is crucial for improving the robustness and generalizability of machine learning models across diverse real-world applications, such as medical image analysis, fault diagnosis, and recommendation systems, where data often exhibits significant domain shifts. The ultimate goal is to reduce the reliance on large, labeled datasets specific to each domain, thereby improving efficiency and reducing annotation costs.