Active Decision Boundary Annotation with Deep Generative Models

TL;DR

This paper introduces a novel active learning approach that involves annotating decision boundaries using deep generative models to generate data points, leading to more efficient training with fewer annotations.

Contribution

The paper proposes a new active learning method that focuses on boundary annotation via generative models, improving annotation efficiency and robustness over traditional sample annotation methods.

Findings

Boundary annotation improves learning efficiency

Method is robust to annotation noise

Can be integrated with existing active learning schemes

Abstract

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.