学习平衡领域泛化内外的特异性和不变性
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization
摘要：
我们引入了特定于领域的泛化掩码，这是一种用于提高域内和域外领域泛化性能的模型。对于领域泛化，目标是从一组源域中学习，以产生一个单一的模型，该模型将最好地泛化到一个看不见的目标域。因此，许多先前的方法专注于学习跨所有源域持续存在的表示，并假设这些域不可知表示将很好地推广。然而，通常单个域包含独特的特征，并且当被利用时可以显著地帮助域内识别性能。为了产生一个最好的推广到可见和不可见域的模型，我们提出了学习域特定的掩码。鼓励掩码学习域不变特征和域特定特征的平衡，从而使模型能够受益于专用特征的预测能力，同时保留域不变特征的普遍适用性。与PACS和DomainNet上的幼稚基线和最先进的方法相比，我们展示了具有竞争力的性能。
英文摘要：
We introduce Domain-specific Masks for Generalization, a model for improving both in-domain and out-of-domain generalization performance. For domain generalization, the goal is to learn from a set of source domains to produce a single model that will best generalize to an unseen target domain. As such, many prior approaches focus on learning representations which persist across all source domains with the assumption that these domain agnostic representations will generalize well. However, often individual domains contain characteristics which are unique and when leveraged can significantly aid in-domain recognition performance. To produce a model which best generalizes to both seen and unseen domains, we propose learning domain specific masks. The masks are encouraged to learn a balance of domain-invariant and domain-specific features, thus enabling a model which can benefit from the predictive power of specialized features while retaining the universal applicability of domain-invariant features. We demonstrate competitive performance compared to naive baselines and state-of-the-art methods on both PACS and DomainNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
