NDLI: Progressive learning with style transfer for distant domain adaptation

Content Provider	IET Digital Library
Author	Xiang, Suncheng Fu, Yuzhuo Liu, Ting
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.
Starting Page	3527
Ending Page	3535
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 14, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0166
Journal	IET Image Processing
Publisher Date	2020-09-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision Computer Vision And Image Processing Technique Deep Unsupervised Progressive Learning Framework Distant Domain Adaptation Task Distant Domain Transfer Learning Domain Adaptation Model EDUPL End-to-end DUPL Image Representation Intermediate Domain Irrelevant Source Domain Knowledge Engineering Technique Labelled Data Labelled Image Novel Transfer Learning Problem Optical, Image And Video Signal Processing Oxford5k Buildings Data Sets Style Transfer Target Domain Traditional Transfer Learning Unrelated Task Unseen Task Unsupervised Learning
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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