Deeper, Broader and Artier Domain Generalization

Da Li, Yongxin Yang, Yi-Zhe Song, Timothy Hospedales

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The problem of domain generalization is to learn from multiple training domains, and extract a domain-agnostic model that can then be applied to an unseen domain. Domain generalization (DG) has a clear motivation in contexts where there are target domains with distinct characteristics, yet sparse data for training. For example recognition in sketch images, which are distinctly more abstract and rarer than photos. Nevertheless, DG methods have primarily been evaluated on photo-only benchmarks focusing on alleviating the dataset bias where both problems of domain distinctiveness and data sparsity can be minimal. We argue that these benchmarks are overly straightforward, and show that simple deep learning baselines perform surprisingly well on them. In this paper, we make two main contributions: Firstly, we build upon the favorable domain shift-robust properties of deep learning methods, and develop a low-rank parameterized CNN model for end-to-end DG learning. Secondly, we develop a DG benchmark dataset covering photo, sketch, cartoon and painting domains. This is both more practically relevant, and harder (bigger domain shift) than existing benchmarks. The results show that our method outperforms existing DG alternatives, and our dataset provides a more significant DG challenge to drive future research.
Original languageEnglish
Title of host publicationThe International Conference on Computer Vision (ICCV 2017)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages5543-5551
Number of pages9
ISBN (Electronic)978-1-5386-1032-9
ISBN (Print)978-1-5386-1033-6
DOIs
Publication statusPublished - 25 Dec 2017
Event2017 IEEE International Conference on Computer Vision - Venice, Italy
Duration: 22 Oct 201729 Oct 2017
http://iccv2017.thecvf.com/

Publication series

Name
PublisherIEEE
ISSN (Electronic)2380-7504

Conference

Conference2017 IEEE International Conference on Computer Vision
Abbreviated titleICCV 2017
Country/TerritoryItaly
CityVenice
Period22/10/1729/10/17
Internet address

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