Data Shift

Data shift, the discrepancy between training and real-world data distributions, significantly hinders the reliability and performance of machine learning models. Current research focuses on detecting and mitigating these shifts through various approaches, including adversarial learning, meta-analysis of existing detection scores, and the development of robust model training methods that incorporate prior knowledge or data augmentation techniques like unproportional mosaicing. Addressing data shift is crucial for improving the generalizability and trustworthiness of machine learning models across diverse applications, particularly in high-stakes domains like healthcare and autonomous systems where model failures can have serious consequences.