Title: Active Decision Boundary Annotation with Deep Generative Models,
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.