Transfer Learning via Latent Dependency Factor for Estimating PM 2.5

arXiv:2404.07308v1 Announce Type: new
Abstract: Air pollution, especially particulate matter 2.5 (PM 2.5), is a pressing concern for public health and is difficult to estimate in developing countries (data-poor regions) due to a lack of ground sensors. Transfer learning models can be leveraged to solve this problem, as they use alternate data sources to gain knowledge (i.e., data from data-rich regions). However, current transfer learning methodologies do not account for dependencies between the source and the target domains. We recognize …

abstract air pollution arxiv cs.lg data developing countries health matter pollution public public health sensors solve transfer transfer learning type via