# Generative Adversarial Data Programming

arXiv: https://arxiv.org/abs/2005.00364 · HTML: https://mlsanity.com/paper/2005.00364/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Label Augmentation with Reinforced Labeling for Weak Supervision](https://mlsanity.com/paper/2204.06436/) (2022)
- [Curating Naturally Adversarial Datasets for Learning-Enabled Medical Cyber-Physical Systems](https://mlsanity.com/paper/2309.00543/) (2023)

## Abstract (arXiv)

The paucity of large curated hand-labeled training data forms a major bottleneck in the deployment of machine learning models in computer vision and other fields. Recent work (Data Programming) has shown how distant supervision signals in the form of labeling functions can be used to obtain labels for given data in near-constant time. In this work, we present Adversarial Data Programming (ADP), which presents an adversarial methodology to generate data as well as a curated aggregated label, given a set of weak labeling functions. More interestingly, such labeling functions are often easily generalizable, thus allowing our framework to be extended to different setups, including self-supervised labeled image generation, zero-shot text to labeled image generation, transfer learning, and multi-task learning.