Domain generalization refers to the problem where we aim to train a model on data from a set of source domains so that the model can generalize to unseen target domains. Naively training a model on the aggregate set of data (pooled from all source domains) has been shown to perform suboptimally, since the information learned by that model might be domain-specific and generalize imperfectly to target domains. To tackle this problem, a predominant approach is to find and learn some domain-invariant information in order to use it for the prediction task. In this paper, we propose a theoretically grounded method to learn a domain-invariant representation by enforcing the representation network to be invariant under all transformation functions among domains. We also show how to use generative adversarial networks to learn such domain transformations to implement our method in practice. We demonstrate the effectiveness of our method on several widely used datasets for the domain generalization problem, on all of which we achieve competitive results with state-of-the-art models.
翻译:域概略是指我们打算对一组源域的数据进行模型培训,以便该模型能够向看不见的目标域推广。对一组数据(来自所有源域)的模型进行模拟培训,显示其具有亚优性,因为该模型所学信息可能是特定域的,对目标域来说不完全。为了解决这一问题,一个主要办法是寻找和学习一些域变量信息,以便用于预测任务。在本文中,我们提出了一个理论上基于的方法,通过在域间所有变异功能下强制建立代表网络,学习域内变异性代表。我们还展示了如何利用基因对抗网络学习这种域变异性,以实践我们的方法。我们展示了我们的方法在几个广泛使用的域通用问题数据集上的有效性,在这些数据集中,我们与最先进的模型取得了竞争性的结果。