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.

翻译：随着深度学习技术的不断发展，数据效率的需求日益凸显。考虑到大规模数据集的标注既耗时又昂贵，主动学习（AL）通过迭代选择信息量最大的样本子集来训练深度神经网络，从而降低标注成本，为这一挑战提供了有前景的解决方案。然而，不同AL算法在不同数据场景下的有效性差异显著，如何确定最适合给定任务的AL算法仍是一个难题。本文提出了首个可微分的主动学习策略搜索方法，命名为AutoAL，该方法构建于现有AL采样策略之上。AutoAL由两个神经网络组成，分别称为SearchNet和FitNet，它们在一个可微分的双层优化框架下同时进行优化。对于任意给定任务，SearchNet和FitNet利用已标注数据迭代协同优化，学习一组候选AL算法在该任务上的表现优劣。在识别出最优AL策略后，SearchNet从未标注池中选择一个小子集以查询其标注，从而实现任务模型的高效训练。实验结果表明，与所有候选AL算法及其他选择性AL方法相比，AutoAL始终能获得更高的准确率，展现了其在不同任务和领域中适应并整合多种现有AL方法的潜力。代码将在以下地址公开：https://github.com/haizailache999/AutoAL。