# Active Decision Boundary Annotation with Deep Generative Models

arXiv: https://arxiv.org/abs/1703.06971 · HTML: https://mlsanity.com/paper/1703.06971/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2017.

## Built on by (most cited first, 8 of 8)

- [Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries](https://mlsanity.com/paper/2103.14824/) (2021)
- [A Survey on Active Deep Learning: From Model-driven to Data-driven](https://mlsanity.com/paper/2101.09933/) (2021)
- [Active Learning Under Malicious Mislabeling and Poisoning Attacks](https://mlsanity.com/paper/2101.00157/) (2021)
- [Semi-supervised lane detection with Deep Hough Transform](https://mlsanity.com/paper/2106.05094/) (2021)
- [REAL: A Representative Error-Driven Approach for Active Learning](https://mlsanity.com/paper/2307.00968/) (2023)
- [Unique Rashomon Sets for Robust Active Learning](https://mlsanity.com/paper/2503.06770/) (2025)
- [Black-box Generalization of Machine Teaching](https://mlsanity.com/paper/2206.15205/) (2022)
- [REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees](https://mlsanity.com/paper/2603.22750/) (2026)

## Abstract (arXiv)

This paper is on active learning where the goal is to reduce the data annotation burden by interacting with a (human) oracle during training. Standard active learning methods ask the oracle to annotate data samples. Instead, we take a profoundly different approach: we ask for annotations of the decision boundary. We achieve this using a deep generative model to create novel instances along a 1d line. A point on the decision boundary is revealed where the instances change class. Experimentally we show on three data sets that our method can be plugged-in to other active learning schemes, that human oracles can effectively annotate points on the decision boundary, that our method is robust to annotation noise, and that decision boundary annotations improve over annotating data samples.