Learning how to generalize the model to unseen domains is an important area
of research. In this paper, we propose TripleE, and the main idea is to
encourage the network to focus on training on subsets (learning with replay)
and enlarge the data space in learning on subsets. Learning with replay
contains two core designs, EReplayB and EReplayD, which conduct the replay
schema on batch and dataset, respectively. Through this, the network can focus
on learning with subsets instead of visiting the global set at a glance,
enlarging the model diversity in ensembling. To enlarge the data space in
learning on subsets, we verify that an exhaustive and singular augmentation
(ESAug) performs surprisingly well on expanding the data space in subsets
during replays. Our model dubbed TripleE is frustratingly easy, based on simple
augmentation and ensembling. Without bells and whistles, our TripleE method
surpasses prior arts on six domain generalization benchmarks, showing that this
approach could serve as a stepping stone for future research in domain
generalization.

本篇论文提出了 TripleE 方法，旨在扩大学习数据集，包括学习复盘和扩增方法，该方法简单易于实现，通过增强数据空间，优于其他领域泛化方法。