Residual Mapping

Residual mapping techniques focus on learning and leveraging the difference (residual) between an initial prediction and a desired outcome, rather than directly learning the complete mapping. Current research explores applications across diverse fields, including autonomous driving (using residual maps from different viewpoints for motion segmentation), solving partial differential equations (by learning residual operators), and accelerating video processing (through recursive residual estimation). This approach enhances efficiency and accuracy in various tasks by focusing computational resources on refining initial estimates, leading to improved performance in areas like robotic surgery simulation and large language model fine-tuning.