Out-of-Domain Generalization From a Single Source: An Uncertainty Quantification Approach

Area of Science:

• Machine Learning
• Artificial Intelligence
• Computer Vision

Background:

• Model generalization is challenging with limited training data across diverse domains.
• Out-of-domain generalization requires models to perform well on unseen data distributions.

Purpose of the Study:

• To address the out-of-domain generalization problem with a single training domain.
• To develop a method that improves model robustness and generalization capabilities.

Main Methods:

• Proposing Meta-Learning based Adversarial Domain Augmentation (MADA).
• Utilizing adversarial training to generate challenging, fictitious data populations.
• Employing a meta-learning framework with a Wasserstein Auto-Encoder for efficient augmentation.
• Integrating uncertainty quantification to enhance domain generalization.

Main Results:

• Demonstrated superior performance on multiple benchmark datasets for single domain generalization.
• The proposed method effectively tackles the worst-case scenario in model generalization.
• Achieved significant improvements in generalizing models to unseen domains.

Conclusions:

• Meta-Learning based Adversarial Domain Augmentation offers a robust solution for out-of-domain generalization.
• The integration of adversarial training and meta-learning provides theoretical guarantees for generalization.
• Uncertainty quantification further boosts the efficiency and effectiveness of domain generalization.