论文标题
学会在域概括中平衡特异性和不变性
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization
论文作者
论文摘要
我们介绍了特定于域的掩模以进行概括，这是一个改善内域和范围外泛化性能的模型。对于域的概括，目标是从一组源域中学习，以产生一个最能推广到看不见的目标域的单个模型。因此，许多先前的方法集中于学习表示形式，这些表示表示在所有源领域都存在，假设这些域不可知论表示会很好地推广。但是，通常，各个领域包含独特的特征，并且杠杆率可以大大帮助内域识别性能。为了产生最能推广到可见和看不见的域的模型，我们建议学习域特定的掩模。鼓励口罩学习域不变和特定于域特征的平衡，从而使模型能够从专业特征的预测能力中受益，同时保留域不变特征的普遍适用性。与PAC和域内的天真基线和最先进的方法相比，我们证明了竞争性能。
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.
