Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis

TL;DR

AutoSWAP automatically generates diverse, task-specific labeling functions using program synthesis, reducing expert effort and improving data efficiency in behavior analysis tasks.

Contribution

The paper introduces AutoSWAP, a novel framework that synthesizes task-level labeling functions automatically, leveraging domain-specific language and diversity costs to enhance weak supervision.

Findings

AutoSWAP outperforms existing methods in behavior analysis domains.

It requires significantly less labeled data to achieve high performance.

The diversity cost improves the variety and effectiveness of generated labeling functions.

Abstract

Obtaining annotations for large training sets is expensive, especially in settings where domain knowledge is required, such as behavior analysis. Weak supervision has been studied to reduce annotation costs by using weak labels from task-specific labeling functions (LFs) to augment ground truth labels. However, domain experts still need to hand-craft different LFs for different tasks, limiting scalability. To reduce expert effort, we present AutoSWAP: a framework for automatically synthesizing data-efficient task-level LFs. The key to our approach is to efficiently represent expert knowledge in a reusable domain-specific language and more general domain-level LFs, with which we use state-of-the-art program synthesis techniques and a small labeled dataset to generate task-level LFs. Additionally, we propose a novel structural diversity cost that allows for efficient synthesis of diverse sets of LFs, further improving AutoSWAP's performance. 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 LFs that can significantly reduce expert effort for behavior analysis.