Domain Engineering

Domain engineering focuses on adapting and improving machine learning models for specific application domains, addressing challenges like limited labeled data and domain discrepancies. Current research emphasizes techniques such as domain decomposition, adaptation strategies (including leveraging pre-trained language models and synthetic data), and the development of specialized model architectures tailored to the unique characteristics of each domain (e.g., dual-domain networks for image reconstruction, template-based approaches for knowledge representation). These advancements are crucial for enhancing the accuracy, efficiency, and applicability of machine learning across diverse fields, from natural language processing and knowledge representation to medical imaging and 3D modeling.