Weight Interpolation

Weight interpolation is a technique in machine learning that combines the parameters of multiple models to create a new model with improved properties, such as robustness, adaptability, or backward compatibility. Current research focuses on developing dynamic and training-free interpolation methods, leveraging model expertise and incorporating momentum or importance weighting to optimize performance across diverse tasks and datasets, including continual learning and view synthesis. These advancements offer significant potential for enhancing model efficiency, controllability, and reducing catastrophic forgetting in various applications, from natural language processing and image recognition to voice assistants and other machine learning-driven systems.