Learning Generalizable Models via Disentangling Spurious and Enhancing
  Potential Correlations
Na Wang Lei Qi Jintao Guo Yinghuan Shi Yang Gao
Domain generalization (DG) intends to train a model on multiple source
domains to ensure that it can generalize well to an arbitrary unseen target
domain. The acquisition of domain-invariant representations is pivotal for DG
as they possess the ability to capture the inherent semantic information of the
data, mitigate the influence of domain shift, and enhance the generalization
capability of the model. Adopting multiple perspectives, such as the sample and
the feature, proves to be effective. The sample perspective facilitates data
augmentation through data manipulation techniques, whereas the feature
perspective enables the extraction of meaningful generalization features. In
this paper, we focus on improving the generalization ability of the model by
compelling it to acquire domain-invariant representations from both the sample
and feature perspectives by disentangling spurious correlations and enhancing
potential correlations. 1) From the sample perspective, we develop a frequency
restriction module, guiding the model to focus on the relevant correlations
between object features and labels, thereby disentangling spurious
correlations. 2) From the feature perspective, the simple Tail Interaction
module implicitly enhances potential correlations among all samples from all
source domains, facilitating the acquisition of domain-invariant
representations across multiple domains for the model. The experimental results
show that Convolutional Neural Networks (CNNs) or Multi-Layer Perceptrons
(MLPs) with a strong baseline embedded with these two modules can achieve
superior results, e.g., an average accuracy of 92.30% on Digits-DG.