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.