Single shot active learning using pseudo annotators

Yazhou Yang, Marco Loog

Abstract: Highlights•A single shot setting of active learning is addressed, where all the required samples should be chosen in a single shot.•Pseudo annotators, which uniformly and randomly annotate queried samples, are introduced to impel standard active learning algorithms to explore.•The exploratory behavior is further enhanced by selecting the most representative sample via minimizing nearest neighbor distance between unlabeled samples and queried samples.•Excellent performance of the proposed method in comparison with state-of-the-art approaches is demonstrated.