Multi Source Domain Generalization

Multi-source domain generalization (MSDG) aims to train machine learning models that generalize well to unseen data distributions, leveraging data from multiple source domains. Current research focuses on developing methods that effectively learn domain-invariant features while mitigating negative transfer from irrelevant source domains, employing techniques like meta-learning, causal inference, and contrastive learning within various model architectures (e.g., mixture-of-experts, gated domain units). This research is significant because it addresses the critical challenge of deploying machine learning models in real-world scenarios where data distributions inevitably shift, impacting diverse applications such as medical image analysis, text classification, and multi-agent trajectory prediction.