ICRL: independent causality representation learning for domain generalization.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Causal Inference

Background:

• Domain generalization (DG) aims to improve model performance on unseen data distributions.
• Existing DG methods often rely on statistical correlations, leading to shortcut learning and spurious causal relationships.
• Causal inference is increasingly integrated into DG, but feature independence is often neglected.

Purpose of the Study:

• To develop a novel framework for domain generalization that ensures feature independence within causal models.
• To mitigate shortcut learning issues inherent in current DG approaches.
• To improve the robustness and efficiency of models dealing with out-of-distribution data.

Main Methods:

• Designed three independent feature modules using Generative Adversarial Network (GAN) variants: GAN, Wasserstein GAN (WGAN), and WGAN with Gradient Penalty (WGAN-GP).
• Selected the optimal WGAN module for integration into a causal model framework.
• Constructed the Independent Causal Relationship Learning (ICRL) model by combining the WGAN module with causal inference principles.

Main Results:

• The proposed ICRL model, utilizing independent causal representations, demonstrated superior performance compared to the original model.
• Experiments showed significant improvements in both predictive accuracy and computational efficiency.
• The WGAN-based feature module proved most effective in establishing independent causal relationships.

Conclusions:

• The ICRL model effectively addresses shortcut learning in domain generalization by ensuring feature independence.
• Integrating independent feature modules into causal frameworks enhances model robustness for out-of-distribution data.
• The proposed approach offers a promising direction for advancing domain generalization research.