Latent-Geometry-Aware Training for Domain Genearlization

Learning-based decision-making models tend to underutilize information from less frequent scenarios, often resulting in poor domain generalization. Most existing domain generalization methods focus on learning domain-invariant representations, typically assuming access to domain information during training. In this work, we develop a fine-tuning strategy for generic classifiers that enhances domain generalization without requiring any domain information. The key idea is to first (i) jointly learn a feature extractor and a low-dimensional affine subspace that minimizes within-class latent dispersion while maximizing between-class latent dispersion, and then (ii) further fine-tune the model by minimizing within-class logit dispersion while maximizing the classification accuracy. Experiments on the ColoredMNIST, Waterbirds, and UrbanCars benchmarks show that the proposed fine-tuning approach achieves domain-generalization performance comparable to state-of-the-art methods that require domain information during training.