Real World Domain Shift

Real-world domain shift, the discrepancy between training and testing data distributions, is a critical challenge hindering the generalization of machine learning models. Current research focuses on developing methods to improve model robustness to these shifts, employing techniques like domain-aware batch normalization, pseudo-source sample generation for target clustering, and data augmentation strategies inspired by causality or normalization perturbation. These advancements aim to enhance the performance of models across diverse real-world scenarios, impacting fields such as autonomous driving, medical image analysis, and automated program repair by improving the reliability and applicability of AI systems.