Pseudo Domain

Pseudo-domains represent groups of data exhibiting similar characteristics despite originating from different sources, a crucial concept in addressing data heterogeneity across diverse datasets. Current research focuses on leveraging pseudo-domains to improve model generalizability and robustness, particularly in medical image analysis and time-series prediction using techniques like adversarial learning, pseudo-labeling, and neural operators adapted for arbitrary domains. This work is significant for enhancing the reliability and applicability of machine learning models in scenarios with limited labeled data or substantial variations in data acquisition, impacting fields ranging from medical diagnosis to cybersecurity.