Continual Adaptation

Continual adaptation in machine learning focuses on enabling models to learn from new data streams without forgetting previously acquired knowledge, mirroring the lifelong learning capabilities of humans. Current research emphasizes efficient adaptation techniques, often employing parameter-efficient fine-tuning methods like adapters and prompt learning within large pre-trained models (e.g., LLMs, vision transformers), or exploring continual learning algorithms such as those based on Bayesian inference or ensemble methods. This field is crucial for deploying robust and adaptable AI systems in dynamic real-world environments, improving the longevity and performance of models in applications ranging from autonomous driving to medical diagnosis. Addressing challenges like catastrophic forgetting and efficiently handling noisy or shifting data distributions remains a key focus.