Active Finetuning

Active fine-tuning focuses on efficiently adapting pre-trained models to new tasks by strategically selecting a subset of data for annotation and subsequent fine-tuning, minimizing the cost and time associated with large-scale data labeling. Current research emphasizes developing algorithms that intelligently select data points maximizing information gain, often incorporating techniques from active learning and leveraging both labeled and unlabeled data, including methods that consider data distribution and uncertainty. This approach significantly improves the efficiency of model adaptation across various domains, such as computer vision, natural language processing, and robotics, leading to substantial gains in performance with reduced annotation effort.