  Mainstream state-of-the-art domain generalization algorithms tend to
prioritize the assumption on semantic invariance across domains. Meanwhile, the
inherent intra-domain style invariance is usually underappreciated and put on
the shelf. In this paper, we reveal that leveraging intra-domain style
invariance is also of pivotal importance in improving the efficiency of domain
generalization. We verify that it is critical for the network to be informative
on what domain features are invariant and shared among instances, so that the
network sharpens its understanding and improves its semantic discriminative
ability. Correspondingly, we also propose a novel "jury" mechanism, which is
particularly effective in learning useful semantic feature commonalities among
domains. Our complete model called STEAM can be interpreted as a novel
probabilistic graphical model, for which the implementation requires convenient
constructions of two kinds of memory banks: semantic feature bank and style
feature bank. Empirical results show that our proposed framework surpasses the
state-of-the-art methods by clear margins.
