We study the question: How can we select the right data for fine-tuning to a specific task? We call this data selection problem active fine-tuning and show that it is an instance of transductive active learning, a novel generalization of classical active learning. We propose ITL, short for information-based transductive learning, an approach which samples adaptively to maximize information gained about the specified task. We are the first to show, under general regularity assumptions, that such decision rules converge uniformly to the smallest possible uncertainty obtainable from the accessible data. We apply ITL to the few-shot fine-tuning of large neural networks and show that fine-tuning with ITL learns the task with significantly fewer examples than the state-of-the-art.

翻译：我们研究以下问题：如何为特定任务选择合适的数据进行微调？我们将此数据选择问题称为主动微调，并证明其属于转导式主动学习的一个实例，这是对经典主动学习的一种新颖推广。我们提出ITL（基于信息的转导学习）方法，该方法通过自适应采样来最大化从指定任务中获取的信息。我们首次在一般正则性假设下证明，此类决策规则能够一致收敛到可从访问数据中获得的最小可能不确定性。我们将ITL应用于大型神经网络的少样本微调，结果表明：与现有技术相比，采用ITL进行微调能够以显著更少的样本学习任务。