Title: SDT: Specific Domain Training in Domain Generalization. Abstract: Domain generalization (DG) methods aim to achieve generalizability to an unseen target domain by using only training data from the source domains. Although there has been a growing interest to learn from multiple training domains by applying different types of invariance across those domains, the improvements compared to empirical risk minimization (ERM) are almost negligible under controlled evaluation protocols. In this paper, we demonstrate that the disentanglement of spurious and invariant features is a tough task in standard training, since ERM simply minimize the loss and does not exploit invariance among domains. To address the issue, we introduce a simple yet effective method called specific domain training (SDT), which intensifies the trace of spurious features and make them more discernible and exploit masking strategy to decrease their effect. We provide a theoretical and experimental evidence to show the effectiveness of SDT for out-of-distribution generalization. Notably, SDT outperforms previous state of the art \citet{cha2021swad} in DomainNet benchmarks 0.2pp in average. Furthermore, SDT improves accuracy of some domains such as Sketch in PACS, SUN09 in VLCS and L100 in TerraIncognita by clear margins 2.5pp, 3.4pp, and 5.4pp respectively.