Continual Learning for Smart City: A Survey

arXiv:2404.00983v1 Announce Type: new
Abstract: With the digitization of modern cities, large data volumes and powerful computational resources facilitate the rapid update of intelligent models deployed in smart cities. Continual learning (CL) is a novel machine learning paradigm that constantly updates models to adapt to changing environments, where the learning tasks, data, and distributions can vary over time. Our survey provides a comprehensive review of continual learning methods that are widely used in smart city development. The content consists of …

abstract adapt arxiv cities city computational continual cs.ai cs.lg data digitization environments intelligent machine machine learning modern novel paradigm resources smart smart cities smart city survey tasks type update updates