Natural Distribution Shift

Natural distribution shift, the discrepancy between training and real-world data distributions, significantly degrades the performance of machine learning models across various domains, including object detection, question answering, and reinforcement learning. Current research focuses on developing more robust models through techniques like improved model architectures (e.g., exploring the impact of backbone design in object detectors), uncertainty quantification methods (e.g., Monte Carlo Dropout), and distributionally robust optimization algorithms. Addressing this challenge is crucial for deploying reliable AI systems in real-world applications, where data inevitably deviates from idealized training sets, and for developing more generalizable and trustworthy models.