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However, such approaches, which directly optimize the decision boundary, often result in overly complex adversarial decision boundaries that are detrimental to generalization. To deal with this issue, a novel plug\u2010and\u2010play method known as Misclassification\u2010Aware Contrastive Adversarial Training (MA\u2010CAT) from the perspective of data distribution optimization is proposed. MA\u2010CAT leverages supervised decoupled contrastive learning to cluster nature examples within the same class in the logit space, indirectly increasing the margins of examples. Moreover, by taking into account the varying difficulty levels of adversarial training for different examples, MA\u2010CAT adaptively customizes the strength of adversarial training for each example using an instance\u2010wise misclassification\u2010aware adaptive temperature coefficient. 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