Domain Generalization With Adversarial Feature Learning

Haoliang Li, Sinno Jialin Pan, Shiqi Wang, Alex C. Kot; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 5400-5409

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Li_2018_CVPR,
author = {Li, Haoliang and Jialin Pan, Sinno and Wang, Shiqi and Kot, Alex C.},
title = {Domain Generalization With Adversarial Feature Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}