Online Adaptation

Online adaptation focuses on enabling machine learning models to quickly and efficiently adjust their parameters to new, unseen data or environments during deployment, without catastrophic forgetting of previously learned information. Current research emphasizes methods like meta-learning, online self-training, and the use of architectures such as transformers and spiking neural networks to achieve this adaptation, often incorporating techniques from control theory and information theory for improved stability and efficiency. This field is crucial for deploying machine learning in dynamic real-world settings, impacting applications ranging from autonomous driving and robotics to remote sensing and human-computer interaction. The development of robust and efficient online adaptation techniques is vital for creating more adaptable and reliable AI systems.