Real Time Adaptation

Real-time adaptation focuses on enabling machine learning models to continuously adjust their performance in response to dynamic changes in data distribution or environmental conditions, without requiring extensive retraining. Current research emphasizes efficient adaptation techniques, often employing lightweight model architectures like adapters or focusing on parameter-efficient fine-tuning methods such as adapting only a subset of model weights or batch normalization parameters. This field is crucial for deploying robust AI systems in real-world scenarios, particularly in areas like autonomous driving, robotics, and personalized healthcare, where models must handle unpredictable variations and operate with limited computational resources.