  Deep Neural Networks (DNNs) have become pivotal in various fields, especially
in computer vision, outperforming previous methodologies. A critical challenge
in their deployment is the bias inherent in data across different domains, such
as image style and environmental conditions, leading to domain gaps. This
necessitates techniques for learning general representations from biased
training data, known as domain generalization. This paper presents Attend to
eXpert Prompts (A2XP), a novel approach for domain generalization that
preserves the privacy and integrity of the network architecture. A2XP consists
of two phases: Expert Adaptation and Domain Generalization. In the first phase,
prompts for each source domain are optimized to guide the model towards the
optimal direction. In the second phase, two embedder networks are trained to
effectively amalgamate these expert prompts, aiming for an optimal output. Our
extensive experiments demonstrate that A2XP achieves state-of-the-art results
over existing non-private domain generalization methods. The experimental
results validate that the proposed approach not only tackles the domain
generalization challenge in DNNs but also offers a privacy-preserving,
efficient solution to the broader field of computer vision.
