Learning to Generate Novel Domains for Domain Generalization
This addresses the problem of limited domain diversity in domain generalization for machine learning practitioners, offering an incremental improvement over existing methods.
The paper tackles domain generalization by generating pseudo-novel domains to augment limited source diversity, resulting in improved model generalizability that outperforms state-of-the-art methods on four benchmark datasets.
This paper focuses on domain generalization (DG), the task of learning from multiple source domains a model that generalizes well to unseen domains. A main challenge for DG is that the available source domains often exhibit limited diversity, hampering the model's ability to learn to generalize. We therefore employ a data generator to synthesize data from pseudo-novel domains to augment the source domains. This explicitly increases the diversity of available training domains and leads to a more generalizable model. To train the generator, we model the distribution divergence between source and synthesized pseudo-novel domains using optimal transport, and maximize the divergence. To ensure that semantics are preserved in the synthesized data, we further impose cycle-consistency and classification losses on the generator. Our method, L2A-OT (Learning to Augment by Optimal Transport) outperforms current state-of-the-art DG methods on four benchmark datasets.