Global Consistency

Global consistency, in various scientific domains, refers to the coherent and unified nature of data or models across their entirety, ensuring internal agreement and realistic representation. Current research focuses on developing methods to achieve and measure this consistency, employing techniques like optimal transport, graph-based semi-supervised learning, and neural networks with specialized loss functions that penalize inconsistencies between local and global features or predictions. These advancements are crucial for improving the reliability and accuracy of diverse applications, including medical image synthesis, virtual try-on, and federated learning, where maintaining global consistency is vital for robust performance and meaningful interpretations. The development of effective metrics for quantifying global consistency is also a significant area of ongoing investigation.