Domain Generalization via Conditional Invariant Representation

Understand

Domain generalization aims to apply knowledge gained from multiple labeled source domains to unseen target domains.

• The main difficulty comes from the dataset bias: training data and test data have different distributions, and the training set contains heterogeneous samples from different distributions.
• Let $X$ denote the features, and $Y$ be the class labels.
• Existing domain generalization methods address the dataset bias problem by learning a domain-invariant representation $h(X)$ that has the same marginal distribution $\mathbb{P}(h(X))$ across multiple source domains.