Progressive learning with style transfer for distant domain adaptation

Abstract

This article studies a novel transfer learning problem termed distant domain transfer learning. Different from traditional transfer learning which assumes there is a close relation between source and target data, in this study, the objective is to execute an unseen and unrelated task based on a labelled data set training previously without any samples from intermediate domains. To this end, the authors propose deep unsupervised progressive learning (DUPL) framework and its upgraded version, end-to-end DUPL (eDUPL). eDUPL consists of two components, i.e. (i) translating the style of labelled images from irrelevant source domain to the target domain and (ii) learning a domain adaptation model with progressive learning for testing on the target domain. In comparison, eDUPL can integrate the two components of the framework seamlessly. In general, the proposed method is easy to be implemented and can be viewed as a strong convolutional baseline for distant domain adaptation task. Comprehensive experiments based on VeRi Vehicle, CUB-200-2011 Birds and Oxford5k Buildings data sets are conducted and the results indicate that the proposed method robustly achieves state-of-the-art performances compared with existing approaches, which demonstrates the effectiveness and superiority of the proposed algorithm.