Deeper, Broader and Artier Domain Generalization
Tools
Tools
Tools
Li, D., Yang, Y., Song, Yi-Zhe and Hospedales, T.M. (2017) Deeper, Broader and Artier Domain Generalization In: 2017 IEEE International Conference on Computer Vision (ICCV 2017), 22-29 Oct 2017, Venice, Italy.
Full text not available from this repository.
Official URL: http://doi.org/10.1109/ICCV.2017.591
Abstract
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.
| Item Type: | Conference or Workshop Item (Conference Paper) | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Divisions : | Faculty of Engineering and Physical Sciences > Electronic Engineering | |||||||||||||||
| Authors : |
|
|||||||||||||||
| Date : | 2017 | |||||||||||||||
| DOI : | 10.1109/ICCV.2017.591 | |||||||||||||||
| Uncontrolled Keywords : | Benchmark testing; Adaptation models; Painting; Neural networks; Nickel; Machine learning; Cameras | |||||||||||||||
| Related URLs : | ||||||||||||||||
| Additional Information : | Printed proceedings published by Curran Associates Inc. | |||||||||||||||
| Depositing User : | Clive Harris | |||||||||||||||
| Date Deposited : | 30 Jul 2019 13:03 | |||||||||||||||
| Last Modified : | 30 Jul 2019 13:16 | |||||||||||||||
| URI: | http://epubs.surrey.ac.uk/id/eprint/852116 |
Actions (login required)
 |
View Item |
Downloads
Downloads per month over past year