GNN-MoE: Context-Aware Patch Routing using GNNs for Parameter-Efficient Domain Generalization

The introduction of GNN-MoE marks a significant advancement in domain generalization, particularly for Vision Transformers. This innovative approach utilizes a Mixture-of-Experts framework to enhance parameter-efficient fine-tuning, making it easier to adapt pretrained models to new domains without the usual costs associated with standard fine-tuning. This is crucial as it allows for better performance on unseen data, which is a common challenge in machine learning. The use of a Graph Neural Network for routing further optimizes the process, potentially setting a new standard in the field.