Domain Detection

Domain detection focuses on identifying data points that originate from a distribution different from the model's training data, a crucial task for ensuring reliable model performance and safety in various applications. Current research emphasizes developing robust methods for detecting out-of-domain (OOD) data, exploring techniques like contrastive learning, K-nearest neighbor approaches, and Bayesian methods, often leveraging pre-trained language models or vision transformers. Successful domain detection is vital for improving the reliability and safety of AI systems across diverse fields, from automated driving and medical image analysis to conversational AI and social media monitoring.