Transfer Learning based City Similarity Measurement Methods

Abstract

In recent years, in order to solve the problem of deep learning in data deficient cities, especially the cold start problem. Researchers put forward a new idea: transfer the model and knowledge from data abundant cities to data scarce cities, also called urban transfer learning. However, in urban transfer learning, the cost for transferring different target cities and source cities cannot be known in advance. In other words, the effectiveness of urban transfer learning need to be improved. In order to solve this problem, we propose a general method for city similarity measurement in urban transfer learning. Through this method, we carry out transfer learning among the cities with higher degree of similarity, which obviously improve the effectiveness of transfer learning at the data level. At the same time, we have also effectively combined this city similarity measurement method with urban transfer learning, and demonstrated the relevant experiment results.