Open Domain Generalization (ODG) is a challenging task as it not only deals with distribution shifts but also category shifts between the source and target datasets. To handle this task, the model has to learn a generalizable representation that can be applied to unseen domains while also identify unknown classes that were not present during training. Previous work has used multiple source-specific networks, which involve a high computation cost. Therefore, this paper proposes a method that can handle ODG using only a single network. The proposed method utilizes a head that is pre-trained by linear-probing and employs two regularization terms, each targeting the regularization of feature extractor and the classification head, respectively. The two regularization terms fully utilize the pre-trained features and collaborate to modify the head of the model without excessively altering the feature extractor. This ensures a smoother softmax output and prevents the model from being biased towards the source domains. The proposed method shows improved adaptability to unseen domains and increased capability to detect unseen classes as well. Extensive experiments show that our method achieves competitive performance in several benchmarks. We also justify our method with careful analysis of the effect on the logits, features, and the head.

翻译：开放域泛化是一项具有挑战性的任务，它不仅需要应对源数据集与目标数据集之间的分布偏移，还需处理类别偏移。为解决该任务，模型必须学习可泛化至未见领域的表征，同时识别训练阶段未出现的新类别。现有方法多采用多个领域专用网络，导致计算成本高昂。为此，本文提出一种仅需单网络即可处理开放域泛化的方法。该方法采用经线性探测预训练的分类头，并引入两个正则化项，分别作用于特征提取器和分类头。这两个正则化项充分利用预训练特征协同作用，在不显著调整特征提取器的前提下对分类头进行修正，从而保证softmax输出的平滑性，并避免模型偏向源领域。所提方法对未见领域展现出更强的适应性，同时提升了检测未知类别的能力。大量实验表明，该方法在多个基准测试中取得了具有竞争力的性能。我们通过对logits值、特征及分类头的深入分析，进一步验证了所提方法的有效性。