Domain Counterfactual

Domain counterfactuals focus on generating synthetic data points that differ from originals only in their domain characteristics, while preserving other relevant features like labels. Current research explores methods for generating these counterfactuals across various domains, including images and text, often employing generative models with constraints to ensure plausibility and control over the transformation. This research aims to improve model robustness, explainability, and fairness by addressing domain shift issues in machine learning, with applications ranging from medical image analysis to natural language processing tasks. The resulting improvements in model performance and interpretability have significant implications for various fields.