Domain Mixture

Domain mixture research focuses on effectively combining data from different sources or domains to improve model performance, particularly in scenarios where labeled data is scarce or distributions differ significantly. Current research emphasizes hybrid architectures, such as those integrating state-space models and attention mechanisms, along with techniques like mixture-of-experts models and optimal transport for aligning data distributions across domains. These advancements are improving performance in various applications, including natural language processing, fault diagnosis, and multi-agent reinforcement learning in areas like autonomous driving, by enabling more robust and efficient learning from diverse datasets.