Domain Adaptive

Domain adaptation in machine learning focuses on improving the performance of models trained on one dataset (source domain) when applied to a different, but related, dataset (target domain). Current research emphasizes techniques like adversarial training, transfer learning, and the use of pre-trained large language models (LLMs) and foundation models, often incorporating specialized architectures such as transformers and graph neural networks to bridge domain gaps. This field is crucial for addressing the limitations of models trained on limited or biased data, enabling broader applicability across diverse real-world applications, including medical imaging, natural language processing, and remote sensing.