Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domains

Understand

Domain generalization (DG) is a difficult transfer learning problem aiming to learn a generalizable model for unseen domains.

• Recent foundation models (FMs) are robust to many distribution shifts and, therefore, should substantially improve the performance of DG.
• In this work, we study generic ways to adopt CLIP, a Visual-Language Foundation Model, for DG problems in image classification.
• While ERM greatly improves the accuracy with bigger backbones and training datasets using standard DG benchmarks, fine-tuning FMs is not practical in many real-world situations.