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Existing domain generalization aims to learn a generalizable model to performwell even on unseen domains. For many real-world machine learning applications,the data distribution often shifts gradually along domain indices. For example,a self-driving car with a vision system drives from dawn to dusk, with the skydarkening gradually. Therefore, the system must be able to adapt to changes inambient illumination and continue to drive safely on the road. In this paper,we formulate such problems as Evolving Domain Generalization, where a modelaims to generalize well on a target domain by discovering and leveraging theevolving pattern of the environment. We then propose Directional DomainAugmentation (DDA), which simulates the unseen target features by mappingsource data as augmentations through a domain transformer. Specifically, weformulate DDA as a bi-level optimization problem and solve it through a novelmeta-learning approach in the representation space. We evaluate the proposedmethod on both synthetic datasets and realworld datasets, and empirical resultsshow that our approach can outperform other existing methods.

Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment