Single domain generalization (Single-DG) intends to develop a generalizable
model with only one single training domain to perform well on other unknown
target domains. Under the domain-hungry configuration, how to expand the
coverage of source domain and find intrinsic causal features across different
distributions is the key to enhancing the models' generalization ability.
Existing methods mainly depend on the meticulous design of finite image-level
transformation techniques and learning invariant features across domains based
on statistical correlation between samples and labels in source domain. This
makes it difficult to capture stable semantics between source and target
domains, which hinders the improvement of the model's generalization
performance. In this paper, we propose a novel causality-inspired latent
feature augmentation method for Single-DG by learning the meta-knowledge of
feature-level transformation based on causal learning and interventions.
Instead of strongly relying on the finite image-level transformation, with the
learned meta-knowledge, we can generate diverse implicit feature-level
transformations in latent space based on the consistency of causal features and
diversity of non-causal features, which can better compensate for the
domain-hungry defect and reduce the strong reliance on initial finite
image-level transformations and capture more stable domain-invariant causal
features for generalization. Extensive experiments on several open-access
benchmarks demonstrate the outstanding performance of our model over other
state-of-the-art single domain generalization and also multi-source domain
generalization methods.

通过基于因果学习和干预的方法，我们提出了一种新颖的因果启发潜在特征增强方法，用于单域泛化，我们可以生成多样的隐式特征级变换，在潜在空间中更好地补偿对初始有限图像级变换的依赖，捕捉更稳定的领域不变因果特征以实现泛化。