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Domain generalization (DG) techniques strive to attain the ability to generalize to an unfamiliar target domain solely based on training data originating from the source domains. Despite the increasing attention given to learning from multiple training domains through the application of various forms of invariance across those domains, the enhancements observed in comparison to ERM are nearly insignificant under specified evaluation rules. In this paper, we demonstrate that the disentanglement of spurious and invariant features is a challenging task in conventional training since ERM simply minimizes the loss and does not exploit invariance among domains. To address this issue, we introduce an effective method called specific domain training (SDT), which detects the spurious features and makes them more discernible. By exploiting a masking strategy and weight averaging, it decreases their harmful effects. We provide theoretical and experimental evidence to show the effectiveness of SDT for out-of-distribution generalization. Notably, SDT achieves comparable results to SWAD, the state of the art in DomainBed benchmarks. [ABSTRACT FROM AUTHOR]