  Generalizing learned representations across significantly different visual
domains is a fundamental yet crucial ability of the human visual system. While
recent self-supervised learning methods have achieved good performances with
evaluation set on the same domain as the training set, they will have an
undesirable performance decrease when tested on a different domain. Therefore,
the self-supervised learning from multiple domains task is proposed to learn
domain-invariant features that are not only suitable for evaluation on the same
domain as the training set but also can be generalized to unseen domains. In
this paper, we propose a Domain-invariant Masked AutoEncoder (DiMAE) for
self-supervised learning from multi-domains, which designs a new pretext task,
\emph{i.e.,} the cross-domain reconstruction task, to learn domain-invariant
features. The core idea is to augment the input image with style noise from
different domains and then reconstruct the image from the embedding of the
augmented image, regularizing the encoder to learn domain-invariant features.
To accomplish the idea, DiMAE contains two critical designs, 1)
content-preserved style mix, which adds style information from other domains to
input while persevering the content in a parameter-free manner, and 2) multiple
domain-specific decoders, which recovers the corresponding domain style of
input to the encoded domain-invariant features for reconstruction. Experiments
on PACS and DomainNet illustrate that DiMAE achieves considerable gains
compared with recent state-of-the-art methods.
