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The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Domain Generalization with Adversarial Feature Learning

Li, Haoliang, Pan, Sinno Jialin, Wang, Shiqi, Kot, Alex C.·2018·2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
DOI 10.1109/cvpr.2018.00566
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Abstract

In this paper, we tackle the problem of domain generalization: how to learn a generalized feature representation for an "unseen" target domain by taking the advantage of multiple seen source-domain data. We present a novel framework based on adversarial autoencoders to learn a generalized latent feature representation across domains for domain generalization. To be specific, we extend adversarial autoencoders by imposing the Maximum Mean Discrepancy (MMD) measure to align the distributions among different domains, and matching the aligned distribution to an arbitrary prior distribution via adversarial feature learning. In this way, the learned feature representation is supposed to be universal to the seen source domains because of the MMD regularization, and is expected to generalize well on the target domain because of the introduction of the prior distribution. We proposed an algorithm to jointly train different components of our proposed framework. Extensive experiments on various vision tasks demonstrate that our proposed framework can learn better generalized features for the unseen target domain compared with state-of-the-art domain generalization methods.

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Citations

Cites

Research works printed but not linked: 29

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  12. gretton, 2006, A kernel method for the two-sample-problem, NIPS
  13. gretton, 2012, Optimal kernel choice for large-scale two-sample tests, NIPS
  14. lecun, 1998, The MNIST Database of Handwritten Digits
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  24. li, 2015, Generative moment matching networks, ICML
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Cited by

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Metadata

Authors
Li, H., Pan, S. J., Wang, S., & Kot, A. C.
Venue
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2018-06
Pages
5400-5409
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2018.00566
OpenAlex
W2798658180
Cited by
1,213
References
11
FWCI
32.77
Type
conference paper
Language
en
Open access
closed

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