Active Decision Boundary Annotation with Deep Generative Models
This work addresses the data annotation challenge in machine learning for practitioners by offering a more efficient active learning strategy, though it appears incremental as it builds upon existing active learning schemes.
The paper tackles the problem of reducing data annotation burden in active learning by introducing a novel approach where human oracles annotate points on the decision boundary instead of data samples, using a deep generative model to create instances along a 1D line, and shows that this method improves over standard sample annotation on three datasets.
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.