In this paper, we propose a novel DG framework for image classification that leverages contrastive learning and loss scaling. Contrastive learning is employed to disentangle domain-specific and domain-invariant feature vectors. Our classifier is trained exclusively with domain-invariant feature vectors that are robust to domain shifts. Additionally, loss scaling is utilized to prevent overfitting to source domains, thereby enhancing the models generalization capabilities to unseen domains.