Domain Keywords

Domain keywords research focuses on improving the performance and generalizability of machine learning models by strategically managing the relationship between training data (in-domain) and unseen data (out-of-domain). Current research emphasizes developing methods to mitigate "catastrophic forgetting" during continued pre-training and leveraging techniques like transfer learning, self-supervised learning, and meta-learning to enhance model robustness across diverse datasets. This work is crucial for building more reliable and trustworthy AI systems across various applications, from medical image analysis to speech recognition and natural language processing, by addressing the challenges of model generalization and domain adaptation.