Domain Adaptation

Domain adaptation studies how to make a model trained in one setting work in another, where images, sensors, language, populations, or conditions may differ. Common approaches align feature distributions with methods such as MMD, CORAL, or adversarial training; generate pseudo-labels for target data; or fine-tune models with a small labeled set, often using lightweight adapters. Recent work also explores source-free and test-time adaptation, as well as foundation models, across settings such as medical imaging, speech, graphs, remote sensing, and sim-to-real transfer. A central challenge is that alignment can cause negative transfer when label spaces or class proportions differ, so methods must balance target gains with preservation of useful source knowledge and be tested under realistic shifts.