As deep learning continues to evolve, the need for data efficiency becomes increasingly important. Considering labeling large datasets is both time-consuming and expensive, active learning (AL) provides a promising solution to this challenge by iteratively selecting the most informative subsets of examples to train deep neural networks, thereby reducing the labeling cost. However, the effectiveness of different AL algorithms can vary significantly across data scenarios, and determining which AL algorithm best fits a given task remains a challenging problem. This work presents the first differentiable AL strategy search method, named AutoAL, which is designed on top of existing AL sampling strategies. AutoAL consists of two neural nets, named SearchNet and FitNet, which are optimized concurrently under a differentiable bi-level optimization framework. For any given task, SearchNet and FitNet are iteratively co-optimized using the labeled data, learning how well a set of candidate AL algorithms perform on that task. With the optimal AL strategies identified, SearchNet selects a small subset from the unlabeled pool for querying their annotations, enabling efficient training of the task model. Experimental results demonstrate that AutoAL consistently achieves superior accuracy compared to all candidate AL algorithms and other selective AL approaches, showcasing its potential for adapting and integrating multiple existing AL methods across diverse tasks and domains. Code will be available at: https://github.com/haizailache999/AutoAL.

本研究解决了主动学习算法在不同数据场景下效果差异大和选择合适算法的难题。我们提出了一种名为AutoAL的可微分主动学习策略搜索方法，通过两个神经网络在可微分的双层优化框架下共同优化，从而有效识别最优的主动学习策略，显著提高了模型的准确性。此方法展示了在多样任务和领域中整合现有主动学习方法的潜力。

AutoAL：自动化主动学习与可微分查询策略