  Domain generalization aims to learn a model that can generalize well on the
unseen test dataset, i.e., out-of-distribution data, which has different
distribution from the training dataset. To address domain generalization in
computer vision, we introduce the loss landscape theory into this field.
Specifically, we bootstrap the generalization ability of the deep learning
model from the loss landscape perspective in four aspects, including backbone,
regularization, training paradigm, and learning rate. We verify the proposed
theory on the NICO++, PACS, and VLCS datasets by doing extensive ablation
studies as well as visualizations. In addition, we apply this theory in the
ECCV 2022 NICO Challenge1 and achieve the 3rd place without using any domain
invariant methods.
