Obtaining annotations for large training sets is expensive, especially in behavior analysis settings where domain knowledge is required for accurate annotations. Weak supervision has been studied to reduce annotation costs by using weak labels from task-level labeling functions to augment ground truth labels. However, domain experts are still needed to hand-craft labeling functions for every studied task. To reduce expert effort, we present AutoSWAP: a framework for automatically synthesizing data-efficient task-level labeling functions. The key to our approach is to efficiently represent expert knowledge in a reusable domain specific language and domain-level labeling functions, with which we use state-of-the-art program synthesis techniques and a small labeled dataset to generate labeling functions. Additionally, we propose a novel structural diversity cost that allows for direct synthesis of diverse sets of labeling functions with minimal overhead, further improving labeling function data efficiency. We evaluate AutoSWAP in three behavior analysis domains and demonstrate that AutoSWAP outperforms existing approaches using only a fraction of the data. Our results suggest that AutoSWAP is an effective way to automatically generate labeling functions that can significantly reduce expert effort for behavior analysis.

AutoSWAP 是一种自动合成数据高效的任务级别检查函数的框架，通过利用专家知识，将特定的标注函数表示为可重复使用的领域特定语言和更一般化的领域级别标注函数，并利用先进的程序合成技术和少量带标签数据来生成任务级别标注函数，提高了自动标注效率。此外，它提出了一种新颖的结构多样性成本，进一步提高了AutoSWAP的性能，实验表明，AutoSWAP可以在大大减少专家工作量的情况下，显著地减少标注需要。

自动综合多样的弱监督来源，用于行为分析