Domain Performance

Domain performance in machine learning focuses on improving the ability of models trained on one dataset (source domain) to generalize to data from different datasets (target domains). Current research emphasizes mitigating catastrophic forgetting and improving out-of-domain performance through techniques like continual pre-training with optimized data mixture ratios, model merging, and the use of adapters or hypernetworks for parameter-efficient adaptation. These advancements are crucial for deploying models in real-world scenarios where data distributions inevitably vary, impacting fields like natural language processing, speech recognition, and medical image analysis. Improved domain generalization leads to more robust and reliable AI systems across diverse applications.