Single shot active learning using pseudo annotators. (May 2019)

• Record Type: Journal Article
• Authors: Yang, Yazhou; Loog, Marco
• Is Part Of: Pattern recognition. Volume 89(2019:May)
• Page Start: 22
• Page End: 31
• Publication Date: 2019-05
• Subjects: Active learning -- Pseudo annotators -- Random labeling -- Single shot -- Exploration and exploitation -- Minimizing nearest neighbor distance; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.12.027
• Languages: English
• ISSNs: 0031-3203

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. Abstract: Standard active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world applications where human experts are not readily available at all times. In this paper, we consider the single shot setting: all the required samples should be chosen in a single shot and no human annotation can be exploited during the selection process. We propose a new method, Active Learning through Random Labeling (ALRL), which substitutes single human annotator for multiple, what we will refer to as, pseudo annotators. These pseudo annotators always provide uniform and random labels whenever new unlabeled samples are queried. This random labeling enables standard active learning algorithms to also exhibit the exploratory behavior needed for single shot active learning. The exploratory behavior is further enhanced by selecting the most representative sample via minimizing nearest neighbor distance between unlabeled samples and queried samples. Experiments on real-world datasets demonstrate that the proposed method outperforms several state-of-the-art approaches.